Reviewerly/exHarmony huggingface dataset:
Adversarial Example Generation Method for Object Detection in Remote Sensing Images

Object detection in remote sensing images is an essential application of deep learning. However, due to the vulnerability of deep learning models, they are susceptible to adversarial attacks, which can undermine their reliability and accuracy. While significant progress has been made in the field of adversarial attacks, most of the work has focused on image classification tasks due to the complexity of object detection. In this paper, we propose a target camouflage method based on adversarial attacks that can mislead detectors and hide targets with minimal pixel perturbations. Experiments on the DIOR dataset demonstrate the effectiveness of our approach. Our method generates adversarial examples that can successfully fool Faster R-CNN into failing to detect objects with minimal perturbations.
Exemplar-free Incremental Learning For Micro-Doppler Signature Classification

The utilization of machine learning techniques has greatly improved the accuracy of micro-Doppler(m-D) signatures-based radar signal recognition. However, the "catastrophic forgetting" problem commonly exists in data-driven algorithms severely limits the adaptability of recognition algorithms in real-world applications, as models cannot incrementally train and learn new categories. In this paper, we propose an incremental learning method, "Boundary Transfer and Uncertainty Augmentation (BTUA)" for continuous learning of m-D signatures. BTUA utilizes boundary transfer(BT) to generate pseudo-decision boundaries for old categories and avoid the forgetting problem. It also employs an uncertainty augmentation(UA) algorithm to enhance the model’s generalization and improve the correctness of feature extraction. Finally, the validation demonstrates the advantages of our algorithm in terms of both accuracy and practicality.
Knowledge Distillation for Memory-Efficient On-Board Image Classification of Mars Imagery

The amount of data captured in the emerging satellite missions has been continuously growing. Thus, developing resource-efficient predictive models is of paramount importance in an array of onboard space applications, where downlinking the data for further analysis is extremely costly or impossible. In such scenarios, we should extract actionable items on board an edge device, using e.g., a machine learning model. Reducing the model’s complexity which may be significant in deep learning algorithms is not only about fitting a full-size neural net into resource-restrictive hardware, but it may result in decreasing the latency and energy consumption. We tackle this issue and exploit knowledge distillation to elaborate a simpler and memory-efficient version of the large-capacity learner, aiming to preserve the large model quality. The experimental study shows that knowledge distillation may not only improve the classification capability of the original model, but can also dramatically (up to 425×) reduce its size for the on-board classification of Mars imagery.
A Bag of Tricks for Fine-Grained Roof Extraction

In this article, we introduce the method we used in the 2023 IEEE GRSS Data Fusion Contest Track 1. The task demands a fine-grained classification method for semantic urban reconstruction. Our experiments are based on Swin transformer, combined with Double-Head module and RFLA (Gaussian Receptive Field based Lable Assignment) strategy, which can effectively improve model's performance on small objects. Experimental results show that our method can bring significant improvement. We achieved the 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> place in the final leader board.
Perceptual Loss for Training Multi-Image Super-Resolution

Super-resolution (SR), i.e. reconstruction of high-resolution (HR) images from low-resolution (LR) samples, plays a vital role in remote sensing. Multi-image SR (MISR) methods leverage information fusion from a series of LR images acquired over time to reconstruct HR images. Deep neural networks form the foundation of state-of-the-art MISR techniques, and selecting an appropriate metric for training them is crucial to obtain reliable results. In this paper, we propose a novel approach for training MISR networks by incorporating the learned perceptual image patch similarity (LPIPS) metric as the loss function. Traditionally, SR methods rely on metrics based on pixel-wise similarity, which may not be optimal when LR and HR images differ significantly in histogram characteristics. By utilizing the LPIPS metric, which is consistent with human perception, the training process is directed towards better reconstruction of image details rather than strict pixel-wise compliance. Experimental results on the MuS2 dataset, consisting of LR images from Sentinel-2 and HR images from WorldView-2, demonstrate the effectiveness of our approach.
Eurosat Model Zoo: A Dataset and Benchmark on Populations of Neural Networks and Its Sparsified Model Twins

The availability of large-scale labeled datasets in remote sensing and Earth observation accelerated the use of deep neural networks in this domain. In the standard workflow, data is first downloaded from satellites and then processed or analyzed locally on Earth. However, the advent of commercial satellite mega-constellations in low-earth orbit, from corporations such as SpaceX or Planet, renders the downstream link of the data for analysis as the limiting factor. Downstream capacity thus becomes a crucial bottleneck for Earth observation. Given this situation, potential future paradigms would require to run deep neural networks on the satellite itself and only download the results of data processing or analysis to Earth. In this scenario, data processing and analysis capabilities are limited by the constraints in compute and power supply of the on-board computer. This work investigates the potential of neural network sparsification techniques to reduce model size and improve efficiency in the Earth observation domain. We study neural network sparsification through the use of populations of neural networks, i.e., thousands of models trained on satellite imagery, to derive insights into the performance of sparsified neural networks for Earth observation.
RESCUENet-VQA: A Large-Scale Visual Question Answering Benchmark for Damage Assessment

In order to advance the research on AI-assisted efficient damage assessment during a natural disaster, we present in this study a large-scale visual question answering (VQA) dataset on remote sensing images, namely RescueNet-VQA. Visual question answering is the task of getting query-based scene information from images. The main advantage of this approach is that it can provide high-level scene information while interacting with users. For this merit, VQA has the potential to be considered in the decision-making processes for rapid response and recovery during any disaster. To conduct substantial research in this context, we present a novel VQA dataset for damage assessment on remote sensing imagery. Images in our dataset were collected after hurricane Michael. We have generated 1,03,192 image-question-answer triplets from 4,375 images. This dataset is the only large-scale remote-sensed imagery-based visual question-answering dataset for damage assessment purposes. We have presented image collection and question generation procedures along with dataset statistics in this work.
Mapping Lithium Bearing Hectorite Clay Using Imaging Spectroscopy

Lithium has been designated as a critical mineral by the U.S. Geological Survey (USGS) due to its importance to the United States economy and over-all risk to supply disruptions [1]. While not currently a major producer of lithium, the United States has significant lithium deposits that could benefit from further investigation [2]. Currently, lithium is primarily extracted from brines of arid sedimentary basins and from granitic pegmatites, but additional potential sources of lithium include lithium-rich clays and zeolites, as well as geothermal and oilfield brines [2]. Remote sensing data enable exploration for and characterization of lithium in clays exposed at the surface, especially hectorite - the most common lithium-bearing clay [2].
Incremental Learning of Remote Sensing Target Classification with Class Hierarchy

Incremental learning can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks. Incremental remote sensing target classification aims to accurately classify newly added classes while maintaining the classification performance of the old classes. In this paper, we propose a new incremental learning method using the information of class hierarchy (CH) and the strategy of Learning without Forgetting (LWF), named CH-LWF. As we know, the target classes in remote sensing images always have hierarchical relationships and are organized as hierarchical tree structures, and CH has been proven to benefit target classification. Our work in this paper may be the first to introduce CH into incremental learning of remote sensing images. In addition, LWF can learn new classes only with new training samples, and the training samples for old classes are not required to be reused, which is convenient in real applications.
RA-Net: An Effective Radar Jamming Recognition Method

With the emergence of novel and complex jamming types, jamming recognition as the primary step in radar anti-jamming is facing tremendous challenges. However, traditional methods experience significant difficulties in identifying increasingly complicated jamming types due to excessive manual dependence and inferior generalization performance. To alleviate the above challenges, we propose a novel recognition framework called Residual Attention Network (RA-Net). Specifically, we integrate channel and spatial attention to learn refined feature representations, which benefits the final recognition accuracy. To further optimize our proposed method, we introduce a polynomial loss to learn a robust feature space. Experimental results on simulated datasets with 19 types of jamming have demonstrated improvement of our proposed RA-Net over traditional methods.
Study of Atmospheric Turbidity on Different Algerian Sites Using a Model and MSG Satellite Images

The temporal analysis of solar radiation at ground level provides essential elements on atmospheric transmission and its evolution. This is especially important for various applications such as solar power systems, optical communications, etc. This article therefore deals on the one hand with the estimation of Global Solar Radiation at Ground Level (GSRGL) from MSG3 satellite images of different spectral channels, a very important parameter for various sites in Algeria considered as privileged areas for the conversion installation PV. The paper also deals with the estimation of the Linke factor T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</inf> linked to the Angström parameter β which provides information on the quantity of aerosols present in the atmosphere of the sites considered. A reminder of the method previously used to estimate GSRGL is first performed. Improvements made to address outstanding issues are then outlined. We then present a new method for estimating T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</inf> at the time of measurements, an input parameter of the clear sky-model ESRA2 useful for the improved method. The results obtained for T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</inf> using ground meteorological measurements are presented and compared to those obtained from satellite data. The validation of the improved method from GSRGL measurements carried out in Ghardaïa (southern Algeria) is finally presented and discussed. We conclude that the presented method can now be used to estimate GSRGL for potential Algerian solar sites.
PanopticBlue: Transformer-based Camera Sky-Cloud-Weather Recognition and Classification

Weather observations are usually derived from measurements and monitoring by sensors, radars, satellites, and human observation. Remote cameras are also used; however, meteorological experts typically report changes in the sky, clouds, and weather based on these images every hour or longer. Moreover, it is challenging to install additional cameras in more locations due to the lack of such experts. Because of the complexity of cloud textures, state-of-the-art (SOTA) Deep Learning (DL) models have been applied to limited cloud types, mainly in clear weather conditions. In addition, sky, cloud, and weather recognition from camera images typically implement multiple DL models. However, SOTA DL models have not considered the impact of weather changes, i.e., rain. This paper proposes PanopticBlue, an integrated transformer-based camera-sky-cloud-weather image recognition and classification model under adversarial weather conditions. Scene object recognition is also used to reduce cloud-type classification errors by DL. Experimental results show the superiority of the proposed DeepTex for many cloud types, i.e., unique cloud recognition, twilight cloud recognition, and Cb type cloud, and weather changes, i.e., rain, lightning, and fog, in enhanced accuracy and robustness over SOTA DL models. Moreover, vision language is used to enrich auto-briefing in weather conditions.
Machine Learning Predictive Models for Phosphate Exploration

Machine learning methods particularly, partial least squares regressions (PLSR) and Support Vector Machine (SVM), were conducted in this study for the abundance prediction of phosphate minerals. Abundances of fluorapatite and dolomite minerals were predicted using VNIR-SWIR hyperspectral reflectance and abundance results issued from X-Ray Diffraction analysis. The performance statistics of the generated PLSR models, for both fluorapatite and dolomite minerals, revealed by the X-Ray Diffraction analysis, were calculated. According to the results, the short-wave infrared Short Wave InfraRed SWIR region has been shown to be the most important for the prediction of dolomite and fluorapatite contents. The PLSR model, developed for the fluorapatite content prediction, has shown an interesting performance compared to the once based on the SVM method. For the dolomite, the SVM model shoed the better results.
Toward Open-World Semantic Segmentation of Remote Sensing Images

In this work, we address the challenge of open-world semantic segmentation for remote sensing (RS) images, which involves segmenting arbitrary objects in images using open RS data. Previous efforts in open-world segmentation mostly focus on Internet-scale paired image-text data with rich vocabulary of concepts. However, these works cannot be directly transferred to RS domain due to the lack of large-scale RS data-text pairs and the corresponding annotations. To overcome this limitation, we propose using text descriptions and annotations from OpenStreetMap as a source of supervision while using images from satellite images. We utilize a conditional Unet model to predict segmentation masks given a text description, and leverage the rich information contained in a pretrained CLIP model to align the images and the corresponding text embeddings using a contrastive loss. Our experimental results demonstrate the potential of open-world segmentation on open RS data.
Unsupervised SAR Despeckling Based on Diffusion Model

Since the deep learning based SAR despeckling models rely heavily on the labeled training data, and struggle to process noisy images with varying noise distribution, this paper proposes an unsupervised SAR despeckling model based on the diffusion model which consists of a forward and a reverse processes. In the forward process, the noise with Gaussian distribution is gradually added to the clear image in the logarithmic domain until the image is heavily contaminated. Then in the reverse process, the noise of the image is gradually predicted and removed by the U-net like neural network until the image is close to the clear image. Furthermore, this paper proposes a shifting and averaging based algorithm for processing high resolution image in patches separately, which gets rid of the dependence on high video memory GPUs. Experiments results demonstrate that the proposed unsupervised despeckling model can be adopted to despeckle SAR images with varying noise intensities simply by adjusting the external parameter values. Though the model’s training does not depend on any clear SAR images, it has close performance compared with advanced supervised models.
Gan-Based Ocean Pattern SAR Image Augmentation

Synthetic Aperture Radar (SAR) image generation using Generative Adversarial Networks (GANs) has gained significant attention in recent years. In addition, the ocean plays a crucial role in regulating Earth’s climate system. SAR images provide valuable information for ocean observation and analysis, aiding in the understanding of oceanic processes and their role in climate change. This study presents a GAN-based approach for generating realistic and diverse ocean pattern SAR images. The proposed methodology combines a style-based generator network with an adversarial discriminator network to learn and reproduce the complex and unique patterns present in SAR images. In order to avoid discriminator overfitting, which frequently occurs as a result of insufficient training data, an adaptive discriminator augmentation (ADA) mechanism has been exploited. By training the GAN with ADA, the generator learns to capture the spatial and statistical properties of oceanic phenomena in restricted data regimes. The experimental results demonstrate the effectiveness and potential of the proposed GAN-based approach for ocean pattern SAR image generation, opening new avenues for advancing ocean observation and analysis in the context of climate change mitigation.
A Framework for Large Scale Semantic Similarity Search on Satellite Imagery

Searching for Earth Science phenomena in large archives of Earth Observation Satellite Imagery data requires elaborate processing and spatio-temporal indexing of the images into categories of the said phenomena. Manual tagging is laborious as it needs constant monitoring through vast volumes of satellite data, the volume and velocity of which is ever-increasing. A complete re-indexing is also needed when a new phenomenon of interest is to be searched through the data archive. Previous efforts to automate tagging have leveraged Machine Learning (ML) techniques to classify images into phenomena of interest. In this method, multiple ML algorithms, each specifically trained for detecting a particular phenomenon, are used for spatio-temporal indexing. While doing so negates the need for human indexing, the process of creating ML models for identifying a class of phenomena involves significant time and computation overhead. Moreover, ML algorithms require vast amounts of extremely scarce labeled data. Furthermore, the computation needed for re-indexing the data whenever a new phenomenon is added to be tagged is not negligible. We propose an alternative, data-driven framework to search through vast amounts of satellite data, that eliminates the need for manual indexing, labeling, or creating purpose-built ML classifiers. The proposed method leverages Self-Supervised Learning (SSL) techniques to obtain feature vectors that are used for search and retrieval of satellite images. An Approximate Nearest Neighbors (ANN) algorithm is used to cluster and retrieve images exhibiting similar features, and by extension, similar Earth Science phenomena. Our unique contribution in this work is the orchestration of the methodology with various cloud services that facilitates searching through millions of images within a short span of time. To showcase the framework, we created a web interface to search through 21 years worth of daily satellite imagery with global coverage. In this paper, we discuss the progress we have made in enabling Embedding Based Search within Remote Sensing, and discuss the potential benefits and pitfalls involved in realizing this method. We also aim to provide insights and experiences we documented while developing such a system along with potential limitations of the current stage of the framework.
GL-NET:Gaussian Leading Network for SAR Ship Detection

Ship detection in synthetic aperture radar (SAR) images is a basic and challenging task in marine monitoring. There has been made remarkable achievements in recent years. However, the existing detection methods still have the problem of ambiguous location information. It leads to the lack of model pertinence, and the poor discrimination of foreground and neighboring background. In this paper, we propose a ship detection method based on FSAF. In particular, we design a Gaussian Leading Block (GLB) capable to eliminate the interference of neighboring background information in the ground truth. Experiments on the HRSID dataset show that the proposed method achieves a 3.6% Average Precision (AP) improvement over the baseline at a low cost.
Moving Object Detection by Low-Rank Analysis of Region-Based Correlated Motion Fields

This paper proposes a novel approach for moving object detection in video sequences captured by nonstationary cameras. The approach, called RCMFD, uses region-based correlated motion fields decomposition, which exploits the sparsity of foreground motions against the low-rank structured background motion. The method uses spatial correlations of region-based features to boost accurate change detection for motion estimation, and motion features across object boundaries are used to exploit moving objects. A dense optical field, which is robust to illumination changes and noise, is established using cross-correlation of region-based features, and a robust principal component analysis (RPCA) model is applied to partition exploited motions into background and foreground motions. Experiments demonstrate the robustness of the proposed method on real video sequences.
DG-YOLOT: A Lightweight Density Guided YOLO-Transformer for Remote Sensing Object Detection

Deep learning-based object detection methods in natural image datasets have demonstrated remarkable accuracy and lower error rates than those of humans. As a result, they have gained significant attention in the field of remote sensing imagery. However, direct transferablity of these methods in remote sensing images face challenges such as scale variations, complex object distributions, and arbitrary orientations. In order to address these challenges, we propose the transformer-based object detector named as DG-YOLOT where we use a guided self-attention mechanism with YOLOv5 to enhance the potentiality of training with minimum computation. Instead of employing uniform size patches like the conventional vision transformer, we leverage density map patches which facilitates the extraction of diverse contextual information related to objects within the image, enhancing the differentiation capability of our model. Through extensive experiments conducted on the DOTAv2.0 dataset, our proposed model has demonstrated superior performance with 57.91% mean Average Precision (mAP) compared to other state-of-the-art object detectors.
Geospatial Policy in India: Impact, Opportunities and Challenges in Education

Geospatial technology holds immense potential for transforming the overall business and educational sector in India. Recently, a National Geospatial Policy was framed and adapted in India. Although, policy claims to foster plethora of opportunities. However, there are few challenges hindering the effective implementation of geospatial policy in various business sector as well as in education field. This paper presents overview of National Geospatial Policy of India with various provisions to upboost business, research, and education in India. Also, the impact, opportunities, and challenges for adaptation of this policy with respect to education are addressed.