Reviewerly/exHarmony huggingface dataset:
Fine-Grained Airplane Recognition in Satellite Images based on Task Separation and Orientation Normalization

One of the common tasks in remote sensing research is object recognition, i.e., detection followed by classification, in satellite images. Object recognition algorithms are widely used in various industrial and military applications. We contribute to the academic research by focusing on airplane recognition in satellite images. In our paper, we utilize existing state-of-the-art deep learning models combined with (1) separating detection and classification and (2) normalizing the input image data concerning rotation in a way that the head of an aircraft always points in the same given direction. This leads to a considerable improvement of both detection and classification accuracy of airplanes as demonstrated by the results from the FAIR1M data set.
Annotation Cost Efficient Active Learning for Content Based Image Retrieval

Deep metric learning (DML) based methods have been found very effective for content-based image retrieval (CBIR) in remote sensing (RS). For accurately learning the model parameters of deep neural networks, most of the DML methods require a high number of annotated training images, which can be costly to gather. To address this problem, in this paper we present an annotation cost efficient active learning (AL) method (denoted as ANNEAL). The proposed method aims to iteratively enrich the training set by annotating the most informative image pairs as similar or dissimilar, while accurately modelling a deep metric space. This is achieved by two consecutive steps. In the first step the pairwise image similarity is modelled based on the available training set. Then, in the second step the most uncertain and diverse (i.e., informative) image pairs are selected to be annotated. Unlike the existing AL methods for CBIR, at each AL iteration of ANNEAL a human expert is asked to annotate the most informative image pairs as similar/dissimilar. This significantly reduces the annotation cost compared to annotating images with land-use/land cover class labels. Experimental results show the effectiveness of our method. The code of ANNEAL is publicly available at https://git.tu-berlin.de/rsim/ANNEAL.
Refining Semantic Granularity of Aerial Image Segmentation Datasets Based on Ground Information

This paper presents a novel approach for refining the semantic granularity of remote sensing image segmentation datasets using openly available cadastral data, thus minimizing the annotation effort. A two-step method is proposed: (1) a supervised learning step for training a semantic segmentation model on fine-grained cadastre labels to extract class prototypes, and (2) an unsupervised learning step utilizing hierarchical clustering on the extracted prototypes to generate a class hierarchy. The method is demonstrated on building segmentation in aerial imagery data, resulting in models capable of predicting new aggregated semantic classes without extra effort in dataset annotation. Evaluation of the proposed method shows its ability to generate meaningful hierarchical relationships among labels and achieve high segmentation performance.
EnMap Geometric Processing and Geometric Performance after One Year of Acquisitions

The German Hyperspectral Satellite Mission EnMAP (Environmental Mapping and Analysis Program; www.EnMAP.org) is an imaging spectroscopy remote sensing mission with the primary objective to measure, derive and analyze quantitative diagnostic parameters describing key processes on the Earth’s surface (see, e.g., [1]). In this article, an overview of the EnMAP processing chain is given with a focus on the geometric processing of EnMAP data. Furthermore, the geometric performance after one year of acquisitions is assessed and presented.
CO-Detector: Towards Complex Object Detection with Cross-Part Feature Learning in Remote Sensing

Object detection in remote sensing imagery builds the essential foundation of aerial and satellite image understanding, being an important role in many common real-world tasks and attracting world-wide attention. In recent years, despite the great progress of common object detection in remote sensing and the proven success of deep learning in this field, yet complex object detection which consists of multiple objects with variable layouts in remote sensing (e.g., coal-fired power plant, airport, sewage treatment plant, etc.) is still challenging for complex composite spatial relationship, non-rigid boundaries, and complicated surrounding textures. These challenges necessitate developing specific complex object detection methods to learn inter-relationship and distinctive and discriminative features in complex objects. To address this problem, in this paper, we propose a method, i.e., CO-Detector, in an end-to-end manner, to achieve various complex composite object detection in remote sensing images with high accuracy and efficiency. The effectiveness of CO-Detector is built on three main parts: (a) First, as surrounding contexts are normally complicated and similar to complex objects, we propose a Tandem Attention Network (TAN), including a channel enhanced network and a spatial enhanced network, with a K-global max/average pooling, to restrain noise disturbance and highlight complex object features and boundaries. (b) Second, we design a Part Region Proposal Network (P-RPN) to learn the interrelationship between parts in one object, generating part proposals and locating discriminative and distinctive object parts finely. (c) Third, to detect the whole complex object as well as the parts, we propose a Part Detection Network (PDN) to detect the individual parts, and detect the whole object through multi-level fused features. We train our CO-Detector model with three selected categories (i.e., coal-fired power plant, airport, oil storage tank) in three datasets, and conduct comparative experiments to evaluate and verify the performance. The comprehensive experiment results show that our CO-Detector achieves a mAP of 80.23%, outperforming 4.17%-17.83% against other cutting-edge deep learning-based detection methods. The experiment results indicate our CO-Detector has promising performance and potential in various complex object detection in highresolution remote sensing images, pending to be utilized in real large-scale applications.
Spectral Unmixing and The Potential of Worldview-3 Satellite Data for Pegmatite Exploration

Remote Sensing has been successfully applied in the identification of pegmatitic targets. However, the spatial resolution of open data satellites, which is often much larger than the outcrop size of the target mineral or rock, has been a recurrent challenge in this scientific field. This restricts remote sensing methods that are dependent on large outcrop sizes for successful identification. This work applied spectral unmixing approach on WorldView-3 satellite imagery, to evaluate the potential for high spatial resolution imagery on the Tysfjord Niobium-Yttrium-Fluorine (NYF) pegmatite field (Norway). The preliminary results of this research are encouraging and make a strong contribution to the scientific field of mineral exploration.
Hazy Remote Sensing Image Restoration Based on Saliency-Guided Transmission Optimization and Texture Boosting

Remote sensing images (RSIs) are susceptible to haze, losing the spectral fidelity and texture details. Haze removal is highly desired in the follow-up tasks such as target identification and semantic segmentation. Most previous works adopted a unified dehazing method to process the entire image and could not fully restore the rich texture information contained in RSIs. In this paper, we propose a hazy RSI restoration method based on saliency-guided transmission optimization and texture boosting. First, we design a saliency-guided transmission optimization method, which achieves different degrees of dehazing to areas with different saliency, fully obtaining the texture and spectral information of RSIs. Second, we propose a saliency-guided atmospheric light (AL) correction method, which fuses the AL of salient regions and non-salient regions to avoid excessive energy attenuation. Finally, a saliency-guided RSI texture boosting method is introduced, further enhancing the texture details of the dehazed RSIs. The efficiency of the proposed method is validated by comparing its performance with six state-of-art schemes.
Lightweight CNN for Cross-View Geo-Localization Using Aerial Image

In the field of remote sensing, there has been a significant amount of research focused on linking different domains such as multi-resolution, multi-spectral, and multi-sensor imagery. One task that involves such multimodal data is cross-view geo-localization, which aims to identify the location of ground query images by matching them with aerial images in a database that is tagged with GPS information. Our study presents a novel lightweight convolutional neural network architecture that achieves comparable performance to a transformer-based model on a city-scale dataset while reducing the number of parameters without using any data augmentation and transformation. Furthermore, our experimental findings indicate that the presence or absence of the fully-connected layer, which is used for generating attention maps, has minimal influence on the model’s accuracy.The code is available from https://github.com/ryotayagiABC/Light_CVGL.
Multi-objective scheduling of college living water based on improved salp swarm algorithm

In order to achieve the water comfort of university users and the timeliness of water supply scheduling, aiming at the low efficiency and complexity of water supply in the multi-source water supply scheduling system of university domestic water, this paper proposes a solution to construct the scheduling model and optimize the solution. Firstly, considering the social and economic objectives, a water supply scheduling model with the goal of minimizing water shortage rate and water purification compensation is constructed. Then, an optimized solution algorithm based on the Improved Salp Swarm Algorithm (ISSA) model is proposed. Taking the living water optimization scheduling of X college students as the background, this paper conducts modeling and compares Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). The case study shows that ISSA is 47.4% and 92.3% lower than SSA and PSO planning results in terms of total water compensation, respectively, and has stronger solving ability, which provides a new idea for solving the optimization of living water scheduling in universities.
Adaptive Cost Adjustment for SAR Imbalanced Classification via Reinforcement Learning

Synthetic aperture radar (SAR) images are difficult to acquire, and the number of images of different targets often varies greatly, resulting in a large number of imbalanced class distributions in practical applications. Existing classification models usually focus too much on the classes with a large number of samples and less on the minority classes with higher-value, which leads to the degradation of classification performance. A novel method for imbalanced classification in SAR images based on reinforcement learning adaptive cost adjustment is proposed in this paper. The method can adaptively search a cost factor and adjust it continuously according to the predicted classification effect so that the performance of all classes of samples is the best possible. This method can obtain a more accurate cost factor and modify the decision boundary of the classifier to achieve a more accurate classification of the minority classes. Experiments on the MSTAR dataset show that the proposed method achieves binary and multi-classification, significantly alleviates the effect of imbalanced data, shows better classification results on all kinds of samples, and outperforms existing methods in overall performance.
Efficient and Accurate Giraffe-Det for UAV Image Based Object Detection

Object detection based on unmanned aerial vehicle (UAV) images has become an important area of research within remote sensing community. However, detecting objects on UAV image datasets, such as Visdrone [1] and UAVDT [2], encounters greater challenges compared with detecting objects on ordinary image datasets like COCO. It can be attributed to the fact that UAV image datasets frequently include a significant quantity of small objects, which are more difficult to detect due to the limited information available. In this study, we introduce a new object detection method for UAV images, termed as HRGiraffe-Det, which builds upon the small-object-friendly detection model(i.e., Giraffe-Det). To preserve more spatial information of small targets, we utilize upsampled image instead of the original image as input. Additionally, we construct a Multi-Proxy Head (MPHead) to deal with objects those have diverse appearance variations. Experimental results on UAV image dataset demonstrate the effectiveness of the proposed method for object detection.
Rotation-Robust Neighbor-Based Subcategory Centroid Alignment for Corss-Domain Scene Classification of Areial Images

Semi-supervised domain adaptation methods can decrease the cost in producing training samples for scene classification. But they may deliver unsatisfactory performances because of the feature distribution bias between the previously labeled data and new aerial images caused by different imaging conditions. In order to address this problem, an original semi-supervised, rotation-robust domain adaptation (SRDA) framework is proposed to decrease the effect of feature distribution bias from the aspect of reducing the effect of rotation variance on representing scenes in aerial images and decreasing the influence of intra-class diversity on land-cover classification. The SRDA framework and some state-of-the-art domain adaptation methods are experimented on the UC Merced dataset to prove its superiority. The experimental results show the superiority of the proposed SRDA method over most of the previous domain adaptation approaches by at least 2% in overall accuracy.
On-Board Image Compression using Convolutional Autoencoder: Performance Analysis and Application Scenarios

The amount of raw data generated by instruments on board Earth Observation (EO) satellites is quite often more than what can be transmitted to the ground, so new advanced on-board processing procedures are required. Artificial Intelligence (AI) and Deep Learning (DL) can provide advanced information from EO data and thanks to specific hardware platforms these algorithms can be used also in space. We present here the Convolutional AutoEncoder (CAE)-based algorithm developed for on-board lossy image compression of the European Space Agency (ESA) Φ-Sat-2 mission. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative on-board environment. Therefore, besides analyzing the results for the local hardware environment, we investigate the performance variation for the on-board setting. Moreover, we introduced an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks.
Enhancing Spatial Resolution of Building Datasets Using Transformer-Based Single-Image Super-Resolution

The spatial resolution of Earth Observation (EO) images plays a key role in building footprint extraction. For the spatial resolution enhancement, deep learning-based image super-resolution methods have been widely used due to their remarkable performance. Transformer-based networks are effective and has drawn much attention in computer vision but underutilized in remote sensing, especially for super-resolving building datasets. Therefore, in this paper, we developed a novel transformer-based Single-Image Super-Resolution (SISR) method, named Pyramid Vision Transformer-Residual Feature Aggregation Network (PVT_RFANet), to improve the spatial resolution of building datasets. Specifically, the PVT v2 network was embedded into our Momentum Spatial-Channel Attention Residual Feature Aggregation Network (MSCA-RFANet). Moreover we conducted a comparative study to compare our method with Bicubic interpolation (BI), Super-resolution Convolutional Neural Network (SRCNN), Deep Recursive Residual Network (DRRN), SRResNet, and MSCA-RFANet. Using Peak Signal-Noise Ratio (PSNR) and Similarity Structure Index Measurement (SSIM) as the evaluation metrics, our method showed highest performance with the PSNR of 22.01 dB and the SSIM of 0.50 on the WHU Building Dataset, which demonstrated the superior performance of the proposed method.
Research on Applications of Digital Virtual Technology in Environment Design

Virtual technology is one of the typical representatives of the new generation of information technology, with characteristics such as immersion, interactivity, and imagination. The application of digital virtual technology in environmental design can increase freedom of environment design, enhance authenticity of environment design and reduce cost of environment design. In response to the slow scene switching time of traditional environmental art design systems, we apply virtual reality technology to the construction of environmental art design systems. The hardware system framework mainly designs collectors, controllers, processors, and data interfaces. The software system makes use of the MATLAB computing software to format different data in the system, and uses binary conversion algorithms to obtain the formatted environmental data. The experimental results indicate that the system has good practical application effects.
Comparative Analysis of CNN Models for Braille Character Classification

Braille character classification is a crucial task with applications in accessibility and assistive technology. In this paper, we present a comprehensive study of different Convolutional Neural Network (CNN) models for the classification of Braille characters. We utilize an image dataset containing 1560 images, with each alphabet represented by 60 images. Our study involves evaluating the performance of SqueezeNet, ResNet-50, Xception, and a custom CNN model that we designed. By comparing the accuracy and effectiveness of these models, we aim to provide insights into their suitability for Braille character classification tasks.
Using Meteosat Cloud Masks for Solar Irradiance Nowcasting

The unpredictable nature of solar irradiance presents a significant challenge for integrating photovoltaic (PV) systems into large-scale energy grids. Short-term solar irradiance forecasting, specifically for 2-6 hours ahead, is crucial for optimizing solar energy integration and improving grid stability. Cloud cover monitoring plays a key role in accurate forecasting of solar irradiance. This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> highlights the utility of satellite-derived cloud mask feature in improving the forcasting accuracy. A lightweight convolutional autoencoder is proposed to encode cloud cover information, enabling nowcasting of solar irradiance values at a 5-minute resolution for a 2-hour lead time. Thus, the approach enhances the accuracy of short-term solar irradiance forecasts with satellite data, facilitating the effective integration of solar energy into energy grids.
Optimal Design of UUV Autonomous Navigation and Positioning Algorithm Based on TDOA Positioning Model

For UUV, the traditional navigation method is to integrate the Dopplervelocitylog (DVL) and inertial navigation system, based on dead reckoning, which usually has some problems, that is, with the increase of time, the error accumulation will become larger and larger. In this paper, the UUV autonomous navigation and positioning algorithm is optimized based on TDOA positioning model. Because UUV has accumulated errors over time, this error has little influence on short-endurance UUV, but for long-endurance UUV, the current velocity in UUV operation area obtained by TDOA positioning model flow estimation method is compensated to UUV navigation speed, which further reduces the positioning error. The data measured by the navigation and positioning system in the actual UUV test are processed and the processing results are given, which proves the correctness of the data processing method. With the aid of TDOA positioning model and flow estimation, UUV autonomous navigation and positioning algorithm can effectively avoid the positioning error caused by sensor failure or fault, and greatly improve the robustness and reliability of UUV navigation and positioning system.
Design of Manipulator Trajectory Tracking Control System Based on Artificial Intelligence Algorithm

In recent years, the continuous development of machine vision technology and manipulator control technology has provided people with fast and efficient services in many fields. In some harsh environments, the manipulator with visual function plays an important role instead of manual work. However, due to the factors such as motion coupling, system friction and strong compressibility of transmission medium, the nonlinear of the system is very strong, which leads to the stagnation of the cylinder in the movement process and the low control accuracy of the system. This article focuses on the problem of complex path types and nonlinear changes. The mobile control system adopts a switching controller, which automatically changes the control algorithm of the robot's turning and moving speed based on the characteristics of different path types. This not only controls the safe walking of the mobile robot on multiple curved and complex paths, but also ensures the accuracy of turning and speed, enabling the mobile robot to effectively track the trajectory. On the premise of ensuring system stability, through simulation modeling and motion analysis of the robotic arm, high-precision trajectory tracking control of parameters such as dual joint angular displacement and angular velocity under various hybrid control algorithms has been obtained. The ultimate goal is to achieve precise control and positioning of the end of the robotic arm.
Decoding the Moon's Surface: A Graph Neural Network Based Analysis of Chandrayaan-2 Lunar Data Classification

This study proposes a fused approach for geological classification of Chandrayaan-2 Imaging Infrared Spectrometer (IIRS) Dataset by combining deep learning and graph-based methods. Convolutional neural networks (CNNs) and Graph Convolutional Networks (GCNs) are utilized to extract features from the spectral information and capture spatial context, respectively. The results from both models are fused to enhance classification accuracy. The proposed approach addresses the challenges of high data dimensionality, limited training data, spectral range limitations, sensor noise, and complex lunar atmosphere. The effectiveness of the approach is evaluated using various accuracy measures.
Quantum Deep Hyperspectral Satellite Remote Sensing

Considering that there are many NP-hard problems in remote sensing (e.g., Craig simplex computation in hyperspectral un-mixing), it is natural to introduce quantum computing into space remote sensing. However, current quantum computers have some practical limitations, requiring complementary techniques to support quantum computing. Specifically, we are introducing artificial intelligence (AI) to solve the quantum collapse effect and the phenomenon of insufficient quantum bits (qubits). We thereby propose the hyperspectral quantum deep network (HyperQUEEN) for satellite remote sensing. HyperQUEEN is the first quantum technology that successfully outputs a complete hyperspectral image, given the very limited qubit resources. Existing quantum image processing methods can only achieve classification-level tasks or simple geometry transforms, and are far from being applicable to advanced satellite missions like restoration of damaged hyperspectral images, which HyperQUEEN has successfully achieved for the first time. Remarkable computational efficiency and restoration performances achieved by the radically new quantum AI system—HyperQUEEN—will be reported.