SOPSeg: Prompt-based Small Object Instance Segmentation in Remote Sensing Imagery

## Abstract

Abstract Extracting small objects from remote sensing imagery plays a vital role in various applications, including urban planning, environmental monitoring, and disaster management. While current research primarily focuses on small object detection, instance segmentation for small objects remains underexplored, with no dedicated datasets available. This gap stems from the technical challenges and high costs of pixel-level annotation for small objects.
While the Segment Anything Model (SAM) demonstrates impressive zero-shot generalization, its performance on small-object segmentation deteriorates significantly, largely due to the coarse 1/16 feature resolution that causes severe loss of fine spatial details.
To this end, we propose SOPSeg, a prompt-based framework specifically designed for small object segmentation in remote sensing imagery. It incorporates a region-adaptive magnification strategy to preserve fine-grained details, and employs a customized decoder that integrates edge prediction and progressive refinement for accurate boundary delineation. Moreover, we introduce a novel prompting mechanism tailored to the oriented bounding boxes widely adopted in remote sensing applications.
SOPSeg outperforms existing methods in small object segmentation and facilitates efficient dataset construction for remote sensing tasks. We further construct a comprehensive small object instance segmentation dataset based on SODA-A, and will release both the model and dataset to support future research.

## Introduction

Remote sensing imagery plays a critical role in a wide range of real-world applications, including urban planning, environmental monitoring, and precision agriculture. Among the targets of interest in these applications, small objects such as vehicles, plane, and ships typically occupy no more than 32×32 pixels in high-resolution imagery, yet they convey essential semantic and operational information for downstream tasks. Consequently, accurately extracting small objects is of great importance, but remains a highly challenging task due to their limited size and complex visual characteristics.

Benchmarks such as SODA-A (Cheng et al. 2023) have significantly advanced small object detection in remote sensing imagery. However, they provide only bounding box annotations, which constrain models to coarse localization and fail to capture precise object shapes. Consequently, most existing works focus on object detection rather than instance segmentation, limiting fine-grained scene understanding.

Instance segmentation for small objects remains largely underexplored, primarily due to the lack of suitable datasets. Constructing such datasets is highly labor-intensive, error-prone, and requires substantial domain expertise. Although the Segment Anything Model (SAM) (Kirillov et al. 2023), trained on over one billion masks, demonstrates strong zero-shot generalization capabilities, its direct application to high-resolution remote sensing imagery leads to notable performance degradation for small objects. We attribute this limitation to the architectural design of SAM: its vision transformer encoder downsamples input images to 1/16 of the original resolution to reduce computational cost. While effective for typical object sizes, this aggressive downsampling results in the loss of fine-grained details that are critical for accurately identifying small targets.

To this end, we propose SOPSeg (Small Object Prompted Segmentation), a novel framework that adapts SAM for robust small-object instance segmentation in remote sensing imagery. Our approach introduces three key innovations: (1) Region-adaptive magnification, which adaptively crops and resizes object regions to preserve fine details lost in downsampling, enabling accurate segmentation of small instances with minimal overhead;
(2) An edge-aware decoder, which integrates boundary prediction and progressive multi-scale refinement to produce sharper and more accurate object masks;
(3) An oriented prompting mechanism, which enables the use of rotated bounding boxes common in aerial imagery, improving SAM’s ability to handle objects at arbitrary orientations.

We train and validate SOPSeg on the iSAID dataset (Waqas Zamir et al. 2019), selecting 7 out of 15 categories that best represent small object challenges in remote sensing imagery. Generalization ability is further evaluated on the NWPU-VHR10 (Su et al. 2019) and SAT-MTB (Li et al. 2023b) benchmarks. Experimental results show that SOPSeg significantly outperforms the original SAM and other prompt-based segmentation methods across all datasets.

Figure: Figure 1: Overview of the SOPSeg Framework. The input remote sensing image is first cropped and resized into multiple high-resolution patches of uniform size. These patches are then fed into the SAM image encoder, where Position Embedding Interpolation (PEI) is applied to support arbitrary input sizes. An oriented prompt, consisting of a horizontal bounding box and three keypoints aligned with the orientation of the object, is encoded via the SAM prompt encoder to guide segmentation. Coarse masks and edges are generated and refined progressively to yield accurate high-resolution segmentation. $\mathbf{F_{d}}$ and $\mathbf{F_{s}}$ denote deep image features from decoder and shallow image features from image encoder.
Refer to caption: CameraReady/LaTeX/Figures/architecture.png

To demonstrate its practical utility, we further apply SOPSeg to assist in constructing a small object instance segmentation dataset. Specifically, we automatically generate approximately 709k instance masks for small objects based on images and oriented bounding boxes from the SODA-A dataset, followed by manual filtering to remove a small number of abnormal annotations. The resulting dataset ReSOS (Remote Sensing Small Object Segmentation), represents the first large-scale instance segmentation benchmark focused on small objects in remote sensing imagery. We plan to publicly release both the model and the dataset to provide training and evaluation resources for future research on small object analysis.

In summary, our contributions are threefold:

- •
We propose SOPSeg, a prompt-based framework that adapts SAM for small object instance segmentation, integrating region-adaptive magnification and edge-aware refinement decoding to enhance mask accuracy.
- •
We develop an oriented prompting mechanism enabling accurate segmentation of objects at arbitrary orientations.
- •
We construct and release ReSOS dataset, the first large-scale instance segmentation dataset specifically designed for small objects in remote sensing. It contains pixel-level annotations for over 709k instances and aims to support future research.

## Related Work

Small Object Detection and Segmentation in Remote Sensing. Small object analysis in remote sensing has attracted significant research interest due to its practical importance. Early detection methods relied on hand-crafted features and traditional machine learning classifiers (Cheng and Han 2016). However, these approaches struggled with the complex backgrounds and varying scales characteristic of aerial imagery. Ding (Ding et al. 2019) proposed a rotation-invariant detector specifically designed for aerial images, while Yang (Yang et al. 2019) introduced SCRDet to handle the multi-scale and multi-orientation challenges. Recent methods have focused on feature enhancement strategies. RMSIN (Liu et al. 2024) employs interaction modules to effectively capture complex spatial scales and orientations for accurate segmentation in remote sensing imagery. For instance, FCOS-RS (Li et al. 2020) adapts the anchor-free FCOS detector for remote sensing by incorporating multi-scale feature fusion. Similarly, Oriented R-CNN (Xie et al. 2021) extends Faster R-CNN (Ren et al. 2016) with oriented region proposals to better capture arbitrarily oriented objects.
Despite progress in detection, instance segmentation of small objects remains largely unexplored. The few existing works primarily focus on specific object categories. Zhang (Zhang et al. 2017) developed a ship instance segmentation method using polar coordinates, while Zhao (Zhao, Persello, and Stein 2021) proposed building extraction techniques. UGBS (Yang et al. 2024) explored interactive user guidance mechanisms to achieve more accurate building segmentation from high-resolution remote sensing images, demonstrating the potential of human-in-the-loop approaches. However, these category-specific approaches do not generalize to diverse small objects. The scarcity of segmentation methods stems from the lack of appropriate datasets and the inherent difficulty of obtaining pixel-level annotations for tiny objects.

Segment Anything Model and Applications. The Segment Anything Model (SAM) (Kirillov et al. 2023) represents a paradigm shift in image segmentation through its foundation model approach. SAM’s versatility stems from its flexible prompting mechanism. Users can specify objects of interest through points, bounding boxes, or coarse masks, enabling interactive segmentation workflows.
Recent works have explored SAM’s potential in remote sensing applications. SAMRS (Wang et al. 2023) leverages SAM to automatically convert object detection datasets into instance segmentation datasets, demonstrating its utility for large-scale annotation tasks. SAM2 (Ravi et al. 2024) enhanced segmentation accuracy on both images and videos. RSPrompter (Chen et al. 2023a) introduces auxiliary prompts specifically designed for remote sensing imagery to improve SAM’s performance. SAM-Adapter (Chen et al. 2023b) proposes lightweight adapters to adapt SAM for domain-specific tasks while preserving its zero-shot capabilities. ROS-SAM (Shan et al. 2025) specifically targets moving object segmentation in remote sensing videos by leveraging LoRA-based adaptation and a context-aware decoder. It primarily focuses on objects with sufficient motion patterns rather than addressing the challenges of small object segmentation. HQ-SAM (Ke et al. 2023) addresses the issue of coarse mask boundaries in the original SAM by introducing a learnable High-Quality Output Token. Matting Anything (Li, Jain, and Shi 2024) extends SAM to the image matting task by predicting precise alpha channels for objects with complex boundaries. Nevertheless, constrained by the resolution of low-level features, existing approaches exhibit limited performance in small object segmentation — a key challenge that this study seeks to systematically tackle.

## Methodology

To bridge the gap between generic segmentation models and the unique demands of small object segmentation in remote sensing, we propose SOPSeg, a prompt-based framework that introduces three key improvements over SAM: a region-adaptive magnification strategy, an oriented prompt mechanism, and an enhanced decoder with integrated edge prediction. The overall architecture is illustrated in Figure [1](https://arxiv.org/html/2509.03002v1#Sx1.F1).

### Region-Adaptive Magnification Strategy

The core challenge in small object segmentation lies in preserving spatial details during feature extraction. SAM’s vanilla image encoder processes images at a fixed resolution, downsampling features to 1/16 of the original size. For small objects occupying only $32\times 32$ pixels, this results in feature representations of merely $2\times 2$ pixels, causing severe information loss.

Figure: Figure 2: (a) The figure illustrates the relationship between the extracted region size $S$ and object dimension $d=max(w,h)$. (b) Illustration of the oriented prompting mechanism for rotated objects. The final prompt points include $P_{1}$, $C$, and $P_{2}$.
Refer to caption: CameraReady/LaTeX/Figures/merge_fig23.png

Our region-adaptive magnification strategy addresses this limitation through adaptive region extraction and resizing. Given an input image and the bounding box $b=(x,y,w,h)$ of a instance, we extract a square local region with boundaries $(x_{s},y_{s},S,S)$, where the size $S$ is determined by the object size $d=\max(w,h)$. Each region is then resized to a fixed resolution of $S_{\text{in}}\times S_{\text{in}}$ before being fed into the model. The relationship between $S$ and $d$ is formulated as:

$$ $S=\begin{cases}k_{0}\cdot d,&\text{if }d<m\\ k\cdot d+(k_{0}-k)\cdot m,&\text{if }d\geq m\end{cases}$ (1) $$

where:
$k=\frac{S_{\text{max}}-k_{0}\cdot m}{S_{\text{max}}-m}$ and satisfies the boundary condition $S=S_{\text{max}}$ when $d=S_{\text{max}}$. A visual illustration is provided in Fig. [2](https://arxiv.org/html/2509.03002v1#Sx3.F2)(a).
We empirically set $m=32$, $k_{0}=2$, and $S_{\text{max}}=1024$. Here, $k_{0}$ represents the initial region expand factor for objects smaller than the threshold $m$. Since all regions are resized to a fixed input size $S_{\text{in}}$, the object magnification is $S_{\text{in}}/S$. Smaller $S$ yields larger magnification, which benefits small objects by enhancing fine details. For objects larger than the threshold, appropriately reducing surrounding context increases their magnification while still retaining essential context. This adaptive design balances detail preservation for small instances and contextual integrity for large ones.

We compute the top-left coordinates $(x_{s},y_{s})$ based on the object’s bounding box center and the desired region size:

$$ $\begin{bmatrix}x_{s}\\ y_{s}\end{bmatrix}=\begin{bmatrix}x\\ y\end{bmatrix}-\begin{bmatrix}a_{x}(S-w)\\ a_{y}(S-h)\end{bmatrix}$ (2) $$

where $a_{x}$ and $a_{y}$ control the object’s position within the extracted region. During training, we set $a_{x},a_{y}\in[0.3,0.7]$ randomly to improve generalization capability, ensuring objects appear at various positions rather than always centered.

SAM’s default $1024\times 1024$ input resolution is designed for processing entire images containing objects of various sizes. However, when focusing on small objects through region extraction, this high resolution becomes computationally wasteful—most pixels represent irrelevant background rather than the target object. Thus we set the input size to $S_{\text{in}}=256$, which preserves sufficient detail for accurate segmentation while significantly reducing computational overhead. For instance, if a small object originally spans $32\times 32$ pixels, the extracted region size $S=64$, and the object is effectively magnified by a factor of $S_{\text{in}}/S=4$.

Position Embedding Interpolation.
Since SAM’s positional embedding weights are input-size dependent, the pretrained embeddings trained on $1024\times 1024$ inputs cannot be directly reused. We address this through bilinear interpolation:

$$ $\text{PE}_{\text{target}}=\text{Interpolate}(\text{PE}_{1024},S_{\text{in}},S_{\text{in}})$ (3) $$

where $S_{\text{in}}=256$ for small object processing. This interpolation preserves the relative spatial patterns while adapting to the new resolution.

The combination of region extraction, magnification, and reduced input resolution creates an efficient pipeline: small objects are first magnified to an adequate size, then processed at lower input size without losing critical details.

### Oriented Prompt Mechanism

Remote sensing objects frequently appear at arbitrary orientations, posing challenges for standard segmentation models. To this end, we propose a strategy that encodes object orientation using strategically placed points, thereby enabling the original SAM—designed for axis-aligned bounding boxes—to effectively handle rotated objects in aerial imagery, all without requiring any modifications to its architecture.

For each oriented bounding box, we extract three key geometric points: the geometric center $C$, and the midpoints $C_{1}$ and $C_{2}$ of the two shorter sides. The line segment $\overline{C_{1}C_{2}}$ naturally defines the object’s principal axis. Apart from $C$, we generate two prompt points along the principal axis:

$$ $P_{1}=\frac{C+C_{1}}{2},\quad P_{2}=\frac{C+C_{2}}{2}$ (4) $$

Fig. [2](https://arxiv.org/html/2509.03002v1#Sx3.F2)(b) illustrates our oriented prompt mechanism on a real example from aerial imagery.

These points encode both spatial and directional information:

- •
The vector $\overrightarrow{P_{1}P_{2}}$ implicitly represents the object’s orientation.
- •
The distance $||P_{1}-P_{2}||$ correlates with the object’s length along its principal axis.
- •
All points remain well within object boundaries, ensuring reliable prompting.

The three points $(P_{1},C,P_{2})$ are directly processed through SAM’s pretrained point encoder:

$$ $E_{\text{points}}=\text{PointEncoder}([P_{1},C,P_{2}])$ (5) $$

Combined with the horizontal bounding box prompt, this provides comprehensive spatial guidance:

$$ $E_{\text{prompt}}=\text{Concat}(\text{BoxEncoder}(b_{\text{horizontal}}),E_{\text{points}})$ (6) $$

This design maintains full compatibility with SAM’s pretrained weights while effectively handling arbitrary orientations. The approach is particularly well-suited for elongated objects prevalent in remote sensing, such as vehicles and ships, where the short-edge midpoints naturally capture the object’s dominant direction.

### Enhanced Decoder with Edge Prediction

Despite the region magnification strategy, small objects in remote sensing imagery still suffer from boundary ambiguity due to complex backgrounds.
We introduce an auxiliary edge prediction path and progressive refinement, enhancing fine-grained delineation of small instances.

Stage 1: Parallel Edge Prediction. We augment the SAM decoder with a learnable edge token $\mathbf{T}_{\text{edge}}$, which collaborates with the original mask tokens $\mathbf{T}_{\text{mask}}$ to capture boundary-specific information. The $\mathbf{T}_{\text{edge}}$, $\mathbf{T}_{\text{mask}}$, and prompt tokens perform bidirectional attention with the image features, resulting in updated representations: $\mathbf{T}_{\text{edge}}^{(1)}$, $\mathbf{T}_{\text{mask}}^{(1)}$, and $\mathbf{F}_{d}$. These are then used to generate two parallel outputs:

$$ $\displaystyle\mathbf{M}_{0}$ $\displaystyle=\text{MLP}_{\text{mask}}(\mathbf{T}_{\text{mask}}^{(1)})\cdot O_{\text{mask}}(\mathbf{F}_{d})$ (7) $\displaystyle\mathbf{E}_{0}$ $\displaystyle=\text{MLP}_{\text{edge}}(\mathbf{T}_{\text{edge}}^{(1)})\cdot O_{\text{edge}}(\mathbf{F}_{d})$ (8) $$

Here, $\text{MLP}_{\text{edge}}$ and $O_{\text{edge}}$ follow the same architectural design as the mask prediction modules in SAM. $\mathbf{M}_{0}$ and $\mathbf{E}_{0}$ denote the initial mask and edge predictions at a resolution of 1/4 the input size.

Stage 2: Progressive Refinement. Initial predictions capture basic structure but lack fine details critical for small objects. We employ multi-scale refinement that gradually improves both masks and edges through iterative processing.
The refinement takes four inputs: deep image features $\mathbf{F}_{d}$ after attention from the decoder, shallow features $\mathbf{F}_{s}$ from the image encoder, the original image $\mathbf{I}$, and the initial predictions $\mathbf{P}_{0}=[\mathbf{M}_{0};\mathbf{E}_{0}]$ from Stage 1.

Both the shallow and deep image features first undergo $2\times$ upsampling and channel dimension reduction mapping for efficient processing. The shallow features $\mathbf{F}_{16s}$ ($\mathbf{F}_{s}$) are processed through convolution, normalization, and $2\times$ upsampling to produce $\mathbf{F}_{8s}$. Similarly, decoder features $\mathbf{F}_{16d}$ ($\mathbf{F}_{d}$) are mapped and upscaled to $\mathbf{F}_{8d}$.

Multi-Scale Refinement. The refinement operates across three spatial scales: $1/8\rightarrow 1/4\rightarrow 1/2\rightarrow 1/1$, progressively enhancing both mask and edge predictions.

- •
Scale 1/8 to 1/4. We concatenate the upsampled features $\mathbf{F}_{8s}$ and $\mathbf{F}_{8d}$ with the downsampled image $\mathbf{I}_{8}$ and predictions $\mathbf{P}_{8}$, and pass them through the first residual refinement block $\mathcal{R}_{1}$:
$\mathbf{X}_{4}=\mathcal{R}_{1}([\mathbf{F}_{8s};\mathbf{F}_{8d};\mathbf{I}_{8};\mathbf{P}_{8}])$
(9)
The refined feature $\mathbf{X}_{4}$ is then mapped to updated predictions $\mathbf{P}_{4}=[\mathbf{M}_{4};\mathbf{E}_{4}]$ via the output head $\phi_{1}$:
$\mathbf{P}_{4}=\phi_{1}(\mathbf{X}_{4})$
(10)
- •
Scales 1/4 to 1/2 to 1/1. We apply the same refinement pattern iteratively. At each scale $i\in\{4,2\}$, we use a residual refinement block $\mathcal{R}_{j}$ and an output head $\phi_{j}$ to generate updated predictions:
$\mathbf{X}_{i/2}=\mathcal{R}_{j}([\mathbf{X}_{i};\mathbf{I}_{i};\mathbf{P}_{i}]),\quad\mathbf{P}_{i/2}=\phi_{j}(\mathbf{X}_{i/2})$
(11)
where $\mathbf{I}_{i}$ is the image and prediction maps downsampled to resolution $1/i$, while $\mathbf{P}_{i}$ is the output from last iteration.
- •
IoU Prediction. At the final stage, the refined feature $\mathbf{X}_{1}$ is also used to predict the mask quality score $p_{\text{iou}}$ via a lightweight head consisting of a convolutional layer, ReLU activation, adaptive average pooling, and linear projection:
$p_{\text{iou}}=\text{IoU}(\mathbf{X}_{1})$
(12)

Optimization Objective. As a whole, we adopt a multi-task loss function that jointly supervises mask prediction, edge localization, and mask quality estimation:

$$ $\mathcal{L}=\sum_{i\in\{1,2,4\}}\left(\mathcal{L}_{\text{mask}}^{i}+\mathcal{L}_{\text{edge}}^{i}\right)+\lambda_{\text{iou}}\mathcal{L}_{\text{iou}}$ (13) $$

**Table 1: Comparison of IoU (%) for different methods on iSAID dataset. Notes: LV-Large Vehicle, SV-Small Vehicle**
| Method | GFLOPs | Params | Ship | Storage Tank | LV | SV | Helicopter | Swimming Pool | Plane | mIoU |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SAM | 1342 | 308M | 73.02 | 74.00 | 71.11 | 60.28 | 56.85 | 64.19 | 64.29 | 66.25 |
| SAM2 | 840 | 216.9M | 73.20 | 72.51 | 70.32 | 59.60 | 57.29 | 65.87 | 63.71 | 66.07 |
| ROS-SAM | 1594 | 359.7M | 81.61 | 81.41 | 78.94 | 72.35 | 61.31 | 79.10 | 76.52 | 75.89 |
| UGBS | 2172 | 79.4M | 84.82 | 86.85 | 85.06 | 81.77 | 66.04 | 82.69 | 78.83 | 80.87 |
| SOPSeg | 1244 | 311M | 87.14 | 88.54 | 87.23 | 85.28 | 67.55 | 84.34 | 80.63 | 82.96 |

Here, both $\mathcal{L}_{\text{mask}}^{i}$ and $\mathcal{L}_{\text{edge}}^{i}$ are composed of a sum of Binary Cross-Entropy (BCE) and DICE (Milletari, Navab, and Ahmadi 2016) losses between the predicted outputs and the ground truth at scale $i$. The ground-truth edge map is derived from the binary mask annotations and smoothed using a $3\times 3$ Gaussian filter to mitigate aliasing artifacts.

The term $\mathcal{L}_{\text{iou}}$ employs a Smooth L1 loss between the predicted IoU score and the actual IoU computed from the original-resolution mask, guiding the model to produce accurate mask quality estimations.
The hyperparameter $\lambda_{\text{iou}}$ balances the contributions of different loss components. In our implementation, we set $\lambda_{\text{iou}}=5.0$.

Figure: Figure 3: Visualization results of small object segmentation methods on iSAID dataset.
Refer to caption: CameraReady/LaTeX/Figures/visual.png

## Experiments

### Experimental Setup

Datasets.
We conduct prompted instance segmentation experiments on three representative remote sensing datasets: iSAID (Waqas Zamir et al. 2019), NWPU-VHR10 (Su et al. 2019), and SAT-MTB (Li et al. 2023b), all of which provide instance-level annotations for aerial or satellite imagery. The iSAID dataset is used for both training and evaluation, while NWPU-VHR10 and SAT-MTB serve as benchmarks for assessing the generalization ability of our method.

To focus on typical small object categories prevalent in remote sensing scenarios, we select specific classes from each dataset. For iSAID, we include: ship, plane, helicopter, small vehicle, large vehicle, storage tank, and swimming pool. For NWPU-VHR10, we consider: ship, airplane, vehicle, and storage tank. For SAT-MTB, we evaluate on ship and plane.

Evaluation Metrics.
We evaluate segmentation performance using mean Intersection over Union (mIoU) and boundary IoU (BIoU) (Cheng et al. 2021) across all instances. BIoU specifically measures boundary quality by focusing on pixels near object edges. We set the dilation ratio of 0.005 to account for the limited spatial extent of small objects.

Comparison Method.
We compare SOPSeg with several representative prompt-based segmentation methods. SAM (Kirillov et al. 2023) is included as the foundational model for interactive segmentation, while SAM2 (Ravi et al. 2024), which extends SAM to video segmentation, also provides more efficient image segmentation. To evaluate domain-specific adaptations to remote sensing imagery, we include ROS-SAM (Shan et al. 2025). For CNN-based prompt segmentation method in remote sensing, we compare with UGBS (Yang et al. 2024). Since UGBS relies on one instance region as input, we adopt our proposed RAM strategy to extract the surrounding regions, enabling a fair evaluation. In addition, we compare detail enhancement methods such as MatAnything (Li, Jain, and Shi 2024) and HQ-SAM (Ke et al. 2023) to analyze the effectiveness of their decoders relative to ours.

Implementation Details.
We use the image encoder and prompt encoder from SAM-Large and freeze their parameters during training. The enhanced decoder, initialized from SAM-Large weights, trained with learning rates of $5\times 10^{-5}$. The progressive refinement module is trained from scratch with learning rates of $1\times 10^{-3}$. The model is trained for 32 epochs using the AdamW optimizer and a cosine annealing learning rate schedule, with a batch size of 24.

### Comparison with Other Methods

Table [1](https://arxiv.org/html/2509.03002v1#Sx3.T1) compares SOPSeg with existing prompted segmentation methods on the iSAID dataset. The results show that SOPSeg outperforms all other methods across all categories. For small vehicle class SOPSeg achieves an IoU of 85.28%, surpassing UGBS by 3.5% and SAM2 by 25.7%. In addition to accuracy, we compare the computational complexity (GFLOPs on 10 instance on a image) and model sizes. SOPSeg achieves the highest mIoU (82.96%) while maintaining a moderate GFLOPs (1244) and parameter size (311M), introducing only 3M additional parameters upon the SAM backbone..

The figure [3](https://arxiv.org/html/2509.03002v1#Sx3.F3) shows visualization results of small object prompt-based segmentation on the iSAID dataset, with all instances overlaid. Due to aggressive feature downsampling, SAM suffers from object adhesion, especially on small targets like cars. ROS-SAM and UGBS partially alleviate this issue but still struggle with boundary precision and object separation. In the first row, ROS-SAM also incorrectly segments non-aircraft regions. Our method accurately preserves object shapes across various scenes, with clear boundaries and well-separated instances, achieving results closest to the ground truth.

Generalization testing. Table [2](https://arxiv.org/html/2509.03002v1#Sx4.T2) evaluates method generalization on NWPU-VHR10 and SAT-MTB datasets. SOPSeg consistently outperforms all baselines. Compared with the strongest baseline UGBS, SOPSeg achieves a slight improvement on NWPU (+0.42% IoU, +1.16% BIoU), but a significantly larger gain on SAT-MTB (+3.06% IoU, +3.02% BIoU). One possible reason is that SAT-MTB presents more significant distribution differences from iSAID (trained dataset), which may affect UGBS more severely, while our SAM-based architecture remains more robust to such variations.

**Table 2: Generalization results on NWPU and SAT-MTB datasets.**
| Method | NWPU | SAT-MTB |  |  |
| --- | --- | --- | --- | --- |
| IoU | BIoU | IoU | BIoU |  |
| SAM | 77.80 | 67.71 | 53.68 | 51.82 |
| SAM2 | 77.24 | 65.54 | 52.35 | 48.76 |
| ROS-SAM | 82.84 | 75.09 | 68.43 | 67.26 |
| UGBS | 86.13 | 79.33 | 70.32 | 69.54 |
| SOPSeg | 86.55 | 80.49 | 73.38 | 72.56 |

### Ablation Study

Effectiveness of Different Modules.
Table [4](https://arxiv.org/html/2509.03002v1#Sx4.T4) presents the ablation results on the iSAID dataset. We progressively incorporate each component of the SOPSeg framework into a baseline model, which fine-tunes the original SAM decoder using horizontal box prompts.
Adding the Region-Adaptive Magnification (RAM) module improves performance by 7.84% mIoU. The RAM module benefits small vehicle and storage tank, where spatial details are often lost during standard downsampling.
The oriented prompt mechanism adds 2.29% over the RAM-only configuration.
Finally, our enhanced decoder contributes an additional 1.27% improvement. This gain is more evident for classes like plane and helicopter, which exhibit complex boundaries and fine structural details. These results demonstrate that each module brings consistent performance gains, and their combination yields the best overall segmentation performance.

Decoder Component Analysis.
As shown in Table [3](https://arxiv.org/html/2509.03002v1#Sx4.T3), we evaluate different decoder designs by replacing our decoder with various alternatives. Our enhanced decoder achieves the best overall performance, outperforming the original SAM decoder by 1.44% in BIoU and 1.21% in IoU. These results demonstrate that incorporating edge prediction effectively preserves fine-grained details and improves boundary accuracy for small object segmentation.

**Table 3: Evaluation on different decoder architectures using both standard IoU and boundary IoU (BIoU) metrics.**
| Method | IoU | BIoU |
| --- | --- | --- |
| SAM | 84.17 | 80.54 |
| MatAnything | 84.74 | 81.02 |
| HQ-SAM | 85.06 | 81.62 |
| Our Decoder | 85.38 | 81.98 |

Input Resolution Analysis.
Figure [4](https://arxiv.org/html/2509.03002v1#Sx4.F4) analyzes the impact of different input sizes on both segmentation performance and computational efficiency. We evaluate four input resolutions—128, 256, 384, and 512—to determine the optimal configuration for small object segmentation.

Figure [4](https://arxiv.org/html/2509.03002v1#Sx4.F4)(a) shows the per-category performance across different input sizes. An input resolution of 256 achieves the best overall performance across most categories, with particularly strong results for ship, swimming pool, and small vehicle. In contrast, an input resolution of 128 significantly degrades performance for categories like helicopter and plane, which rely heavily on fine-grained spatial details for precise boundary delineation. Interestingly, storage tank performs

**Table 4: Ablation study results on the iSAID dataset. RAM: Region-Adaptive Magnification; OPM: Oriented Prompt Mechanism; EDE: Enhanced Decoder with Edge Prediction.**
| Method | Ship | Storage Tank | Large Vehicle | Small Vehicle | Helicopter | Swimming Pool | Plane | mIoU |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Base | 79.86 | 74.62 | 75.53 | 62.14 | 61.52 | 72.77 | 75.13 | 71.65 |
| +RAM | 83.43 | 85.90 | 83.08 | 79.46 | 64.91 | 80.45 | 78.59 | 79.40 |
| +RAM+OPM | 85.64 | 88.17 | 86.02 | 83.98 | 65.33 | 84.22 | 78.47 | 81.69 |
| +RAM+OPM+EDE | 87.14 | 88.54 | 87.23 | 85.28 | 67.55 | 84.34 | 80.63 | 82.96 |

**Table 5: Comparison of instance segmentation results (AP, %) on our constructed dataset.**
| Method | Plane | Helicopter | Small Vehicle | Large Vehicle | Ship | Container | Storage Tank | Swimming Pool | AP |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SparseInst | 8.6 | 0.1 | 6.6 | 8.3 | 8.7 | 10.8 | 26.7 | 36.9 | 13.3 |
| Mask2Former | 25.0 | 2.2 | 8.4 | 12.1 | 13.6 | 15.2 | 33.5 | 23.8 | 16.7 |
| MaskDINO | 41.0 | 13.1 | 22.9 | 35 | 36.7 | 40.1 | 46.5 | 45.6 | 35.1 |

best at 128 resolution, possibly due to its inherently simple and compact structure, which may become over-smoothed or misrepresented when additional detail is introduced at higher resolutions.
Larger input sizes, such as 384 and 512, do not lead to proportional improvements over 256. Although marginal improvements are observed in the plane category, these come at the cost of significantly higher computational demands.

Figure [4](https://arxiv.org/html/2509.03002v1#Sx4.F4)(b) presents the computational analysis. The mIoU curve peaks at a resolution of 256, confirming it as the optimal choice in terms of accuracy. Meanwhile, GFLOPs (for one instance) increase dramatically with higher resolution.

Figure: Figure 4: The impact of input size on (a) class-wise IoU and (b) mean class IoU and computational cost.
Refer to caption: CameraReady/LaTeX/Figures/combine.png

### Evaluation on the Constructed Dataset

Leveraging the strong small object segmentation capability of the proposed SOPSeg, combined with manual filtering, we constructed the ReSOS (Remote Sensing Small Object Segmentation) dataset based on images and oriented bounding boxes from SODA-A (Cheng et al. 2023), containing pixel-level annotations for over 709k instances. This dataset provides a solid foundation for the evaluation and advancement of small object segmentation techniques. Due to page constraints, a detailed description of the dataset is provided in the appendix.

We evaluated three representative instance segmentation methods on ReSOS, with the Average Precision (AP) results across eight object categories summarized in Table [5](https://arxiv.org/html/2509.03002v1#Sx4.T5). MaskDINO (Li et al. 2023a) achieves the highest overall AP, significantly outperforming Mask2Former (Cheng et al. 2022a) and SparseInst (Cheng et al. 2022b). The results reveal notable performance variations across categories. Storage-tank and swimming-pool classes demonstrate relatively better performance, likely due to their distinctive geometric shapes and clear boundaries that facilitate segmentation. In contrast, the helicopter category is the most challenging, primarily because of its fuzzy boundaries. Small-vehicle segmentation proves especially difficult for conventional methods, indicating that sparse representation alone is insufficient for effectively handling small objects. Moreover, we observe a consistent trend that larger objects tend to achieve higher segmentation accuracy. These findings underscore the urgent need to advance small object instance segmentation techniques.

## Conclusion

We presented SOPSeg, a prompt-based framework for small object instance segmentation in remote sensing imagery. To address the loss of spatial details when objects occupy fewer than $32\times 32$ pixels, we introduced a region-adaptive magnification strategy that preserves fine-grained features. The oriented prompt mechanism enables accurate segmentation of rotated objects without architectural modifications. The edge-aware decoder with progressive refinement enhances mask accuracy. Extensive experiments show that SOPSeg generalizes well across datasets. Ablation studies further validate the effectiveness of each proposed component.
We will release SOPSeg along with our annotated small object dataset ReSOS. This dataset fills a gap in the remote sensing community by providing pixel-level annotations for thousands of small object instances. We hope that the release of SOPSeg and its accompanying dataset will serve as a valuable resource for the community and foster continued research in small object segmentation within remote sensing imagery.

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## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: 1.The author seems to have only compared the model performance with the prompt-based model in the experiment. Can you consider adding some non-this type of model for performance comparison? 
2.In the region adaptive amplification strategy, how to select the object for the cropping and amplification of the small target dense region? 
3.In the region adaptive amplification strategy, if the complete target object in the original image appears incomplete at the edge of the cropping region, how to solve the adverse effects on the model?
4.When the target is no longer an elongated object such as a vehicle and a ship, but an object with an irregular shape, can the midpoint of the short-edge in the oriented prompting mechanism still provide accurate object direction guidance?
soundness: 3
strengths: 1.The SOPSeg proposed by the author has achieved the best performance compared with other prompt-based methods on multiple datasets. 
2.Sufficient ablation experiments verified the performance improvement brought by each module. 
3.The author has constructed a new remote sensing image small target instance segmentation data set to provide support for subsequent research on related tasks.
summary: This paper proposes a new model named SOPSeg for small object instance segmentation of remote sensing images, and produces a new large-scale remote sensing small object instance segmentation dataset ReSOS. Based on the original architecture of SAM, the model introduces an edge-aware decoder, adds a parallel edge prediction branch, and gradually refines the prediction results of the model by combining multi-scale features to improve the instance segmentation effect of small objects. In order to cope with the loss of small object information caused by the downsampling operation in the image encoder, the Region-adaptive magnification(RAM) strategy increases the size of the small object in the input image by cropping and amplifying the size of the region where the small object is located. At the same time, the oriented prompting mechanism(OPM) is used to give the model directional point prompts to cope with the rotating bounding box in the aerial image. Experiments show that the method achieves sota performance.
weaknesses: 1.This paper only compared the model performance with the prompt-based model in the experiment of model performance comparison. 
2.The performance improvement of the SOPSeg mainly comes from the technical processing operations of the two modules of RAM and OPM, rather than the innovation of the model.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 3
questions: See weaknesses above.
soundness: 3
strengths: The paper is easy-to-follow. The explanations on the proposed modules are clear. The experiments report the key metrics of instance segmentation work.
summary: This paper introduces a SAM-style instance segmentation framework for small objects in high-resolution remote sensing images. The proposed framework also extends a previous object detection samples to a new datasets with masks. The experiments on several datasets verified the effectiveness of the additional information extraction and support for the proposed framework.
weaknesses: I have several questions and comments on this paper.

1. Why did the authors select 7 out of 15 categories? Are these categories are all small objects? How about the others? I cannot find a clear definition on the 'small' concept. The missing information probably makes it unclear for readers.
2. How is the generated data quality based on the SOPSeg framework? The authors claimed that they did the manual filtering to refine the samples. However, I cannot find any information about the details regarding the quality and filtering.
3. The authors pointed out previous works cannot handle diverse small objects. However, in the settings from this paper. the categories only cover 7 types. We cannot know the real performance when facing multiple and diverse small objects in remote sensing domain, e.g., panel, window, flag. or road sign.
4. From my understanding, the region-adaptive magnification strategy rely on the coarse bounding box annotation for the precise localization, then it's not fair to directly compare the results with other models as it has additional information fed into the network. Moreover, finding a small object from remote sensing images is not easy, either. which makes the motivation not so convincing.
5. The two prompt points are generated based on the bounding boxes, while the orientation of a object is not always consistent with its mask prediction. They are not 100% correct for a complex object.
6. What is the motivation of the evaluation 4.4? We can know the coarse performance of the popular models on the proposed dataset. However, we don't know the low performance is caused by the data complexity or the restriction of the models on the remote sensing images, or the correctness of the data.
7. The proposed strategies are not new in remote sensing domain, e.g., local cropping, point-orientation, and the progressive refinement.

---

confidence: 5
contribution: 2
initial_rating: 4
presentation: 2
questions: **Q1.** ReSOS masks are generated by SOPSeg then filtered manually. How did you quantify and mitigate method-induced bias/circularity—e.g., percent masks edited/rejected, cross-model re-annotation, and impact on non-SOPSeg methods’ rankings? 

**Q2.** When compared with SAM/HQ-SAM/ROS-SAM/UGBS, are identical oriented prompts (OBB + keypoints) provided for all baselines? If not, please report the results after adding equivalent prompts to isolate the OPM gain and ensure fairness.
soundness: 2
strengths: The paper's main merit lies in identifying and tackling a significant and challenging problem: instance segmentation of small objects in remote sensing imagery. This is an important, practical area where foundational models like SAM are known to struggle, so the motivation is strong and well-founded.

The commitment to construct and release a new dataset, SODA-A-seg, is a laudable goal. The community lacks such a dedicated resource for small object instance segmentation, and if executed well, this could become a valuable contribution. Creating new benchmarks is a critical service to the field.

Conceptually, some of the proposed architectural ideas are sound. The region-adaptive magnification strategy is a logical approach to address the loss of fine-grained details for small objects, which is a known limitation of standard Vision Transformer backbones. Similarly, adapting the prompt mechanism to handle oriented bounding boxes (OBBs) demonstrates an awareness of the specific needs of the remote sensing domain.
summary: This paper addresses the challenging and underexplored task of small object instance segmentation in remote sensing imagery. The authors identify a key limitation in existing models like SAM: their coarse feature resolution leads to a significant loss of detail, hampering performance on small objects.
To overcome this, the paper introduces SOPSeg, a prompt-based framework with three main contributions:

1.**A novel model architecture:** It incorporates a region-adaptive magnification strategy to preserve fine-grained details and a customized decoder that uses edge prediction and progressive refinement for accurate boundaries.

2.**A tailored prompting mechanism:** It is designed to work with oriented bounding boxes, which are prevalent in remote sensing applications.

3.**A new public resource:** The authors construct and will release SODA-A-seg, a dataset for small object instance segmentation, addressing a resource gap in this domain.
weaknesses: While the paper addresses an important problem, its current execution suffers from several major weaknesses that undermine its contributions and call its conclusions into question.

1.**Insufficient Empirical Support for Core Claims:** The paper's central motivation—that SAM's failure on small objects is primarily due to its coarse 1/16 feature resolution—is presented as a fact but is never experimentally verified. A rigorous paper would include an analysis or ablation study to support this claim. For instance, the authors could have evaluated features from earlier, higher-resolution stages of SAM's encoder or compared its performance on small versus large objects within the same dataset to quantify the performance degradation.

2.**Inadequate Experimental Comparisons:** The baseline coverage is too narrow, as the main table compares almost exclusively with SAM series (SAM/HQ-SAM/ROS-SAM) and UGBS, lacking comparisons with non-SAM prompt-based frameworks. This narrow scope weakens the generality claim and raises fairness concerns around prompts; to substantiate generality, include strong non-SAM baselines (e.g., Mask2Former, MaskDINO, SparseInst).

3.**The Dataset Contribution is Unverifiable:** The paper claims the new SODA-A-seg dataset as a major contribution, yet it provides almost no details about it. The text explicitly defers these crucial details—such as dataset size, object categories, object size distribution, annotation protocol, and quality control measures—to an appendix that is not provided with the submission. This is a critical omission. Without this information, it is impossible for reviewers to assess the quality, scale, or significance of this claimed contribution. A contribution that cannot be reviewed is, for all practical purposes, non-existent in the context of this submission.

4.**Poor Presentation Quality and Lack of Clarity:** The overall presentation of the paper falls short of the standards expected at ICLR. There are numerous issues with the layout and formatting, such as the misalignment of Figure 4 and Table 5, which disrupt the flow and suggest a lack of careful preparation. Beyond aesthetics, many figures are poorly explained, lack detailed captions, and are difficult to interpret. This lack of clarity is not merely cosmetic.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: On the region-adaptive magnification strategy:
1)	Would it be possible to learn these parameters dynamically through a lightweight network or reinforcement signal rather than using a predefined rule?
On the oriented prompting mechanism:
1)	The method selects the object center and two midpoints of the short edges as prompts. How sensitive is the segmentation performance to the exact location or number of these points?
2)	In cluttered scenes where objects overlap, how does the prompting mechanism avoid interference between nearby instances?
On the edge-aware decoder and refinement process:
1)	Could the authors clarify how the edge branch interacts with the main mask branch during multi-scale refinement? Is there any feature sharing or fusion between them?
2)	What is the computational overhead introduced by the edge prediction and iterative refinement steps compared with the original SAM decoder?
3)	Have the authors visualized intermediate feature maps or edge activations to show how the refinement improves small-object boundaries?
On the ReSOS dataset construction:
1)	The dataset creation pipeline mentions automatic mask generation followed by manual filtering. Could the authors provide more details on the filtering process—such as the proportion of removed masks, annotator consistency, or validation criteria?
2)	How is annotation quality verified? Were any quality metrics (e.g., IoU between annotators, random spot checks) used?
soundness: 3
strengths: The paper is original in focusing on small object instance segmentation in remote sensing imagery, a problem that has received limited attention. It proposes a reasonable and targeted framework (SOPSeg) that extends the Segment Anything Model with region-adaptive magnification, edge-aware decoding, and oriented prompting, effectively addressing the limitations of existing methods on small targets. The technical design is sound, and the experiments on several public datasets provide convincing evidence of improvement. The construction of the ReSOS dataset also adds practical value by supporting future research. Overall, the paper is clearly presented.
summary: The paper tackles the challenge of small object instance segmentation in remote sensing imagery. This task remains largely unexplored due to the lack of pixel-level annotated datasets and the difficulty of segmenting fine-scale targets. To address this, the authors propose SOPSeg (Small Object Prompted Segmentation), a prompt-based framework that adapts the Segment Anything Model (SAM) for small-object segmentation. SOPSeg introduces three key innovations:
1)	Region-adaptive magnification to preserve fine spatial details lost during downsampling.
2)	An edge-aware decoder that combines boundary prediction and progressive refinement for precise mask generation.
3)	An oriented prompting mechanism tailored for rotated bounding boxes common in aerial imagery.
SOPSeg achieves superior performance over SAM and other prompt-based baselines on multiple datasets, including iSAID, NWPU-VHR10, and SAT-MTB. Furthermore, it is used to construct ReSOS, the first large-scale remote sensing small object instance segmentation dataset, containing over 709,000 pixel-level annotations derived from SODA-A. Overall, the work provides both a novel model and a valuable dataset, advancing research on fine-grained small object analysis in remote sensing.
weaknesses: 1)	Dataset construction lacks transparency.
While the ReSOS dataset is an important contribution, the paper provides limited information on annotation quality control and manual filtering criteria. Details such as the number of annotators, inter-annotator agreement, or filtering ratio would improve reproducibility and trust in dataset quality.
2)	Generalization evaluation could be expanded.
The model is validated on iSAID, NWPU-VHR10, and SAT-MTB, which share similar imaging styles and object categories. Testing on datasets with different sensors or resolutions (e.g., DOTA or FAIR1M) would better demonstrate robustness across varying data domains.
3)	 Computational efficiency discussion is limited.
Although the paper reports GFLOPs, there is no analysis of runtime or memory consumption under different region magnification settings. Since SOPSeg involves region extraction and multi-scale refinement, efficiency and scalability to large scenes remain unclear.
Overall, the work is technically solid and practically useful, but the dataset transparency, and analytical depth could be further improved to strengthen its scientific contribution.