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HASNet: A Foreground Association-Driven Siamese Network With Hard Sample Optimization for remote sensing Image Change Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Tao, Chao Kuang, Dongsheng Huang, Zhenyang Peng, Chengli Li, Haifeng Cent South Univ Sch Geosci & Info Phys Changsha 410205 Peoples R China

remote sensing change detection (RS-CD) relies on the model's ability to learn features of marked change objects, known as foreground targets. Beyond foreground targets, the background targets are more valuable samples for change detection, such as unlabeled ones, semantically ambiguous ones, pseudo-changes, and non-interesting changes, referred to as hard case samples (HCSs) in this article. There are two additional challenges to learning HCSs: 1) the loss function focusing on the foreground targets with rich labels and ignoring the HCSs in the background, called the imbalance problem and 2) it is difficult for a model to learn the change information of HCSs directly, which is called HCSs missingness. This article proposed a foreground association-driven Siamese network with hard sample optimization (HASNet). To deal with the imbalance problem, we propose an equilibrium optimization loss (EO-loss) function to regulate the optimization focus of the foreground and background, determine the HCSs through the distribution of the loss values, and introduce dynamic weights in the loss term to gradually shift the optimization focus of the loss from the foreground to the background hard cases as the training progresses. To address the HCSs missingness, we propose the scene-foreground association module by using potential remote sensing spatial scene information to model the association between the target of interest in the foreground and the related context to obtain scene embedding to reinforce the feature of hard cases. Experiments on four public datasets with 11 baselines show that HASNet outperforms current state-of-the-art CD methods, particularly in detecting HCSs. The source code is available at https://***/GeoX-Lab/HASNet.

关键词： Change detection foreground-scene association hard case samples (HCSs) hard case samples (HCSs) remote sensing image remote sensing image remote sensing image

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Neural Network Based on Dynamic Collaboration of Flows for Temporal Downscaling

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remote sensing 2025年第8期17卷 1434-1434页

作者： Wang, Junkai Lin, Lianlei Zhang, Yu Zhang, Zongwei Gao, Sheng Zhao, Hanqing Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Technol Innovat Ctr Littoral Test Harbin 150001 Peoples R China

Time downscaling is one of the most challenging topics in remote sensing and meteorological data processing. Traditional methods often face the problems of high computing cost and poor generalization ability. The framework interpolation method based on deep learning provides a new idea for the time downscaling of meteorological data. A deep neural network for the time downscaling of multivariate meteorological data is designed in this paper. It estimates the kernel weight and offset vector of each target pixel independently among different meteorological variables and generates output frames guided by the feature space. Compared with other methods, this model can deal with a large range of complex meteorological movements. The 2 h interval downscaling experiments for 2 m temperature, surface pressure, and 1000 hPa specific humidity show that the MAE of the proposed method is reduced by about 14%, 25%, and 18%, respectively, compared with advanced methods such as AdaCof and Zooming Slow-Mo. performance fluctuates very little over time, with an average performance fluctuation of only about 1% across all metrics. Even in the downscaling experiment with a 6 h interval, the proposed model still maintains a leading performance advantage, which indicates that the proposed model has not only good performance and robustness but also excellent scalability and transferability in the downscaling task in the multivariate meteorological field.

关键词： remote sensing data meteorological data time downscaling dynamic collaboration of flows neural network

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CE-CDNet:A Transformer-Based Channel Optimization Approach for Change Detection in remote sensing

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Computers, Materials & Continua 2025年第4期83卷 803-822页

作者： Jia Liu Hang Gu Fangmei Liu Hao Chen Zuhe Li Gang Xu Qidong Liu Wei Wang School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450002China Department of Computing Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

In recent years,convolutional neural networks(CNN)and Transformer architectures have made significant progress in the field of remote sensing(RS)change detection(CD).Most of the existing methods directly stack multiple layers of Transformer blocks,which achieves considerable improvement in capturing variations,but at a rather high computational *** propose a channel-Efficient Change Detection Network(CE-CDNet)to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change *** adaptive multi-scale feature fusion module(CAMSF)and lightweight Transformer decoder(LTD)are introduced to improve the change detection *** CAMSF module can adaptively fuse multi-scale features to improve the model’s ability to detect building changes in complex *** addition,the LTD module reduces computational costs and maintains high detection accuracy through an optimized self-attention mechanism and dimensionality reduction *** test results on three commonly used remote sensing building change detection data sets show that CE-CDNet can reduce a certain amount of computational overhead while maintaining detection accuracy comparable to existing mainstream models,showing good performance advantages.

关键词： remote sensing change detection attention mechanism channel optimization multi-scale feature fusion

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Enhanced multi-level features for very high resolution remote sensing scene classification

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NEURAL computing & APPLICATIONS 2024年第13期36卷 7071-7083页

作者： Sitaula, Chiranjibi Sumesh, K. C. Aryal, Jagannath Univ Melbourne Dept Infrastruct Engn Earth Observat & AI Res Grp 700 Swanton St Carlton Vic 3053 Australia

Very high resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems. Recently, the existing deep learning (DL)-based methods have shown great promise in VHR RS scene classification. However, they still provide an unstable classification performance. To address such a problem, we herein propose a DL-based novel approach using an enhanced VHR attention module (EAM), which captures the richer salient multi-scale information for a more accurate representation of the VHR RS image during classification. Experimental results on three widely used VHR RS data sets show that the proposed approach yields a competitive and stable/consistent classification performance with the least standard deviation of 0.001. Further, the highest overall accuracies on the AID, NWPU, and UCM data sets are 95.39%, 93.04%, and 98.61%, respectively. Such encouraging, consistent and improved results shown through detailed ablation and comparative study provide a solution to the remote sensing community for the land use and land cover (LULC) classification problems with more trust and confidence. The source code of this work is available at https://***/csitaula/EAM.

关键词： Deep learning Aerial image analytics Image classification Transfer learning

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Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第12期35卷 18145-18159页

作者： Fan, Fan Shi, Yilei Guggemos, Tobias Zhu, Xiao Xiang Tech Univ Munich TUM Chair Data Sci Earth Observat SiPEO D-80333 Munich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82234 Wessling Germany Tech Univ Munich TUM Sch Engn & Design D-80333 Munich Germany

Image classification plays an important role in remote sensing. Earth observation (EO) has inevitably arrived in the big data era, but the high requirement on computation power has already become a bottleneck for analyzing large amounts of remote sensing data with sophisticated machine learning models. Exploiting quantum computing might contribute to a solution to tackle this challenge by leveraging quantum properties. This article introduces a hybrid quantum-classical convolutional neural network (QC-CNN) that applies quantum computing to effectively extract high-level critical features from EO data for classification purposes. Besides that, the adoption of the amplitude encoding technique reduces the required quantum bit resources. The complexity analysis indicates that the proposed model can accelerate the convolutional operation in comparison with its classical counterpart. The model's performance is evaluated with different EO benchmarks, including Overhead-MNIST, So2Sat LCZ42, PatternNet, RSI-CB256, and NaSC-TG2, through the TensorFlow Quantum platform, and it can achieve better performance than its classical counterpart and have higher generalizability, which verifies the validity of the QC-CNN model on EO data classification tasks.

关键词： Image classification quantum circuit quantum machine learning (QML) remote sensing imagery

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Masked Second-Order Pooling for Few-Shot remote-sensing Scene Classification

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IEEE GEOSCIENCE AND remote sensing LETTERS 2024年 21卷 1页

作者： Deng, Jianan Wang, Qianli Liu, Nanqing Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Southwest Jiaotong Univ Prov Key Lab Informat Coding & Transmiss Chengdu 611756 Peoples R China

Few-shot remote-sensing scene classification (FSRSSC) is the task of categorizing remote-sensing images (RSIs) with insufficient labeled samples. This task becomes particularly challenging due to low intraclass similarity and high interclass similarity in RSIs. To overcome these challenges, we introduce a novel masked second-order pooling (MSoP) module to exploit the masked second-order features, enhancing feature representation and classification performance for FSRSSC. The MSoP module comprises two key components: a learnable DropBlock (LDB) and a compressed second-order pooling (CSoP). The LDB selectively masks discriminative regions in feature maps, effectively alleviating the low intraclass similarity problem. On the other hand, the CSoP enhances second-order statistics by computing and aggregating channel-wise similarity, thereby reducing the impact of interclass similarity. We construct an MSoP-Net based on the proposed MSoP module. Experimental results demonstrate its superior performance on two widely used datasets, that is, NWPU-RESISC45 and UCM.

关键词： Feature extraction Training Task analysis remote sensing Prototypes Scene classification Convolution DropBlock few-shot learning (FSL) remote-sensing image (RSI) remote-sensing scene classification (RSSC) second-order pooling (SoP)

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Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

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Enhanced remote sensing Monitoring Through a Bimodal Cloud Infrastructure: A Dual-State Cloud Service Approach

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IEEE ACCESS 2024年 12卷 97405-97416页

作者： Yang, Kaijun Lei, Fan Wei, Jide Zhang, Zhe China Univ Geosci Wuhan Inst Adv Studies Wuhan 430078 Peoples R China Second Surveying & Mapping Inst Hunan Prov Changsha 410029 Peoples R China Minist Nat Resources Key Lab Nat Resources Monitoring & Supervis Southe Changsha 410029 Peoples R China

This study addresses significant challenges within the field of remote sensing monitoring, including operational inefficiency, data confidentiality concerns, high hardware costs, and issues with data management and distribution. To tackle these problems, we introduce a synergistic remote sensing monitoring framework that leverages a bimodal cloud infrastructure, facilitated by cloud services to provide on-demand resource allocation and efficient management. Our research focuses on designing and developing an integrated operating system that optimizes remote sensing monitoring processes and enhances operational efficiency through the use of a mutual scheduling mechanism and rapid data indexing capabilities. The system is underpinned by a dual-state cloud service mechanism, combining the Memory Cloud (Flash Cloud) known for its high-speed data processing and the Storage Cloud (Persistent Cloud) for long-term data retention. This dual-state approach establishes a multi-level caching system to ensure quick access to frequently requested spatial data. Additionally, a two-tier security system is implemented to safeguard data integrity and confidentiality. Our "YunYao" geographic information service rendering engine, operating on this dual-state cloud platform, demonstrated remarkable performance advantages over mainstream platforms in identical testing environments. Specifically, it outperformed ArcGIS Desktop by over two times, exceeded GeoServer by more than four times, and was over seven times faster than ArcGIS Server in rendering speeds. Experimental and practical applications have shown that our system streamlines routine workflows and enhances work efficiency, making it a critical reference for remote sensing monitoring. Furthermore, a comparative analysis was conducted to quantitatively demonstrate the superior performance of our method in handling large volumes of remote sensing data(Including satellite imagery and UAV imagery). Despite these advancements, the in

关键词： Cloud computing Monitoring remote sensing Memory management Distributed databases Sensors Rendering (computer graphics) Load management Cloud service remote sensing monitoring bimodal cloud load balancing

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Preferred Feature Representation for remote sensing Scene Classification 24

Preferred Feature Representation for Remote Sensing Scene Cl...

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16th International conference on Machine Learning and computing (ICMLC)

作者： Yang, Meng Chen, Yaxiong Lu, Xiongbo Xiong, Shengwu Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Hubei Peoples R China

ISBN: (纸本)9798400709234

For remote sensing image analysis field, remote sensing scene classification is the main task to label the semantic content of remote sensing images. remote sensing scene images have highly complex geometric structures. Convolutional neural networks can only capture short-range dependencies and cannot adapt to the unique spatial patterns of remote sensing images, leading to insufficient information extraction for more discriminative features. remote sensing images also face difficulties such as large intra-class variability and high inter-class similarity. To address these challenges, we propose a new method for efficiently learning feature representations using two modules: a Feature Preference Module (FPM) for single image data and a Batch Cross-sample Attention Module (BCAM) for multiple image data. This approach aims to enhance the discriminative power of features. We conducted experiments on the ERA, AID, and UC Merced datasets, achieving state-of-the-art performance and demonstrating the effectiveness of our proposed method.

关键词： remote sensing scene classification Convolutional Neural Networks (CNN) Feature Preference Cross-sample Attention

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CUG_MISDataset: A remote sensing Instance Segmentation Dataset for Improved Wide-Area high-Precision Mining Land Occupation Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2024年 17卷 16476-16486页

作者： Zhu, Yuqian Chen, Weitao He, Wenxi Wang, Ruizhen Li, Xianju Wang, Lizhe China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Nat Resource Informat Management & Di Wuhan 430074 Peoples R China Wuhan Ctr China Geol Survey Wuhan 430074 Peoples R China China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Peoples R China

The effective and rapid acquisition of wide-area mine occupation information is crucial for ecological geo-environmental protection and sustainable development. remote sensing instance segmentation technology based on deep learning is a promising solution. However, there are two significant challenges including insufficient training datasets and unsuitable segmentation models. To overcome these issues, this study provides a large-scale remote sensing instance segmentation dataset for mining land occupation (CUG_MISDataset). The CUG_MISDataset comprises 1426 image blocks and more than 3000 instances, covering all 150 types of mines found in China's Hubei province. It features multiple mine types, various land occupations, and complex instance scales. First, this study compares the performance of seven mainstream remote sensing instance segmentation models using the proposed CUG_MISDataset. The results show that all seven models achieve high segmentation accuracy. It indicates that the constructed CUG_MISDataset is robust and can serve as a valuable benchmark for remote sensing instance segmentation of mining areas. Second, aiming at the difficulty of large scale variation in this dataset, we propose a multiscale dilation feature pyramid network (MSD-FPN), which introduces a dynamic weight allocation mechanism to give more weight to important semantic information, while convolution with different dilation rates is used in the module to enhance the expression of mines' multiscale features. The proposed MSD-FPN can achieve a 2.0% average precision improvement on the CUG_MISDataset.

关键词： Deep learning instance segmentation mining area mining land occupation remote sensing

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