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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Liu, Chenchen Cao, Yice Jiang, Dingkun Wu, Zhenhua Hu, He Huang, Zhixiang Anhui University School of Electronic and Information Engineering Hefei230601 China

ISBN: (纸本)9798331515669

The improvement in imaging resolution and the increase in imaging swath of remote sensing satellites have enabled the acquisition of vast and complex remote sensing image data. Effectively and comprehensively extracting target information to enhance segmentation accuracy and interpretation efficiency has become a research hotspot in the field of remote sensing semantic segmentation. Although current segmentation algorithms based on CNN and Transformer frameworks have made significant progress in performance, achieving a good balance between segmentation accuracy and computational complexity remains challenging, limiting their widespread application in practical tasks to a certain extent. To address this issue, this paper introduces a state-space model and proposes an efficient vision Mamba for remote sensing image semantic segmentation, abbreviated as EvMDNet. By designing an efficient vision state space block (EvSSBlock), the method effectively integrates local and non-local features while improving computational efficiency through efficient 2D scanning (ES2D) strategy. Furthermore, to avoid the insufficient utilization of features and weak discriminative ability caused by the direct addition of skip connections, a dynamic feature fusion block (DFFBlock) is designed to adaptively adjust the weights of different features for efficient feature fusion. Experimental results on classical remote sensing image datasets demonstrate that the proposed method not only improves segmentation accuracy but also reduces computational complexity to a certain extent, outperforming existing state-of-the-art segmentation algorithms. © 2024 IEEE.

关键词： Semantic Segmentation

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FRLI-Net: Feature Reconstruction and Learning Interaction Network for Tiny Object Detection in remote sensing images

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IEEE signal processing Letters 2025年 32卷 2159-2163页

作者： Chen, Penglei Wang, Jiangtao Zhang, Zhiwei He, Cheng Huaibei Normal University School of Physics and Electronic Information Huaibei235000 China Huaibei Normal University Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei235000 China

In the field of computer vision, detecting tiny objects in remote sensing images has long been a challenging task. This difficulty primarily stems from the poor matching of tiny objects with specific feature scales, compounded by susceptibility to interference from complex surroundings. To this end, we propose a Feature Reconstruction and Learning Interaction Network (FRLI-Net) for overcoming the aforementioned difficulties in tiny object detection. Specifically, to mitigate information loss of tiny objects in the feature space, we introduce a Feature Reconstruction Supervision Branch (FRSB) used only during training, which utilizes supervised loss to guide the attention of tiny objects within the reconstructed feature regions. Then, to tackle false detections caused by blurred boundaries of tiny objects, we design a Saliency Learning Interaction Module (SLIM) that employs learnable weights and proposed sub-functions to facilitate feature interaction, thereby reducing interference from complex backgrounds in the foreground. Extensive experiments conducted on the remote sensing image datasets AI-TOD-v2 and TinyPerson demonstrate that our FRLI-Net significantly enhances the detection of tiny objects, outperforming other state-of-the-art detection methods. © 2025 IEEE.

关键词： image reconstruction

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MAS-NET:MIXED-FEATURE ATTENTION SIAMESE NETWORK FOR CHANGE DETECTION ON remote sensing imageS 49

MAS-NET:MIXED-FEATURE ATTENTION SIAMESE NETWORK FOR CHANGE D...

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49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ding, Xingyu Wang, Weiqiang Univ Chinese Acad Sci CAS Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Change detection plays a crucial role in remote sensing tasks. However, current deep learning-based change detection methods suffer from issues such as misclassified pixels and unclear segmentation result on edges. To address these challenges, we propose a novel approach called Mixed-feature Attention Siamese Network (MAS-Net). MAS-Net adopts an encoder-decoder structure, where the encoder part effectively fuses image features from different time points, thereby preserving more cross-time information in the feature map. In the decoder part, we introduce the Feature Global Attention Module (FGAM) to leverage the global attention mechanism for extracting deep semantic information from the fused feature map. By incorporating these proposed modules and strategies, MAS-Net achieves fewer misclassified pixels and clearer edges in the resulting change detection maps. Experimental evaluations on the LEvIR-CD and CDD datasets demonstrate that MAS-Net outperforms state-of-the-art models by 0.15% (91.88% vs. 91.73%) and 1.3% (97.5% vs. 96.2%) in terms of F1-Score, respectively, thus establishing a solid baseline for change detection.

关键词： change dectection remote sensing attention mechanism

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MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJECT DETECTION IN remote sensing imageS 31

MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJ...

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2024 International conference on image processing

作者： Bahaduri, Bissmella Ming, Zuheng Feng, Fangchen Mokraoui, Anissa Univ Sorbonne Paris Nord Lab L2TI Villetaneuse France

ISBN: (纸本)9798350349405;9798350349399

Object detection in remote sensing images (RSI) is a critical task for numerous applications in Earth Observation (EO). Differing from object detection in natural images, object detection in remote sensing images faces challenges of scarcity of annotated data and the presence of small objects represented by only a few pixels. Multi-modal fusion has been determined to enhance the accuracy by fusing data from multiple modalities such as RGB, infrared (IR), lidar, and synthetic aperture radar (SAR). To this end, the fusion of representations at the mid or late stage, produced by parallel subnetworks, is dominant, with the disadvantages of increasing computational complexity in the order of the number of modalities and the creation of additional engineering obstacles. Using the cross-attention mechanism, we propose a novel multi-modal fusion strategy for mapping relationships between different channels at the early stage, enabling the construction of a coherent input by aligning the different modalities. By addressing fusion in the early stage, as opposed to mid or late-stage methods, our method achieves competitive and even superior performance compared to existing techniques. Additionally, we enhance the SWIN transformer by integrating convolution layers into the feed-forward of non-shifting blocks. This augmentation strengthens the model's capacity to merge separated windows through local attention, thereby improving small object detection. Extensive experiments prove the effectiveness of the proposed multimodal fusion module and the architecture, demonstrating their applicability to object detection in multimodal aerial imagery. Our code is available at here.

关键词： Multimodal fusion cross-channel attention convolutional shifting window object detection remote sensing imagery

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GANInSAR: Deep Generative Modeling for Large-Scale InSAR signal Simulation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERvATIONS AND remote sensing 2024年 17卷 5303-5316页

作者： Zhou, Zhongrun Sun, Xinyao Yang, Fei Wang, Zheng Goldsbury, Ryan Cheng, Irene Univ Alberta Comp Sci Edmonton AB T6G 2R3 Canada 3V Geomat Inc RnD Vancouver BC V5Y 0M6 Canada

Interferometric synthetic aperture radar (InSAR) technology is widely used to create digital elevation models and measure dynamics on the Earth's surface, including monitoring ground displacements. The lack of or limited-collected ground-truth data, however, often poses a bottleneck in validating the research outcome, particularly at high precision and resolution levels. To mitigate the gap, we introduce a new deep generative model (DGM) for the simulation of linear deformation rate maps. We demonstrate that our adversarial DGM architecture with carefully designed preprocessing and postprocessing modules performs well for InSAR deformation signal synthesis, even when limited data are available. We also introduce a dimensionality reduction method, based on the distance between the real-world and generated image feature vectors, to address the lack of quantitative evaluation for data simulation. Furthermore, we introduce a hybrid evaluation metric integrating quantitative and qualitative measures, which is more intuitive than the existing methods and makes it easier for domain experts to participate in the evaluation. We compare the results of our model with established methods. The comparison result illustrates the superior performance of our proposed method.

关键词： Deformation Generative adversarial networks Atmospheric modeling Deformable models Training Data models Generators Deep generative modeling generative adversarial network (GAN) interferometric synthetic aperture radar (InSAR) radar signal processing remote sensing images simulation

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Koopman Ensembles for Probabilistic Time Series Forecasting 32

Koopman Ensembles for Probabilistic Time Series Forecasting

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32nd European signal processing conference (EUSIPCO)

作者： Frion, Anthony Drumetz, Lucas Tochon, Guillaume Dalla Mura, Mauro El Bey, Abdeldjalil Aissa IMT Atlantique Lab STICC Brest France LRE EPITA Le Kremlin Bicetre France Univ Grenoble Alpes CNRS Grenoble INP Inst Univ FranceGIPSA Lab Grenoble France

ISBN: (纸本)9789464593617;9798331519773

In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate the training of ensembles of models to produce stochastic outputs. We show through experiments on real remote sensing image time series that ensembles of independently trained models are highly overconfident and that using a training criterion that explicitly encourages the members to produce predictions with high inter-model variances greatly improves the uncertainty quantification of the ensembles.

关键词： Dynamical systems Koopman operator Uncertainty quantification remote sensing Sentinel-2

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Triplet Network with Autoencoder for remote sensing image scene recognition and classification

Triplet Network with Autoencoder for Remote sensing image sc...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Hu, Fangchun Dai, Yifeng Wu, Lie Anhui University Anhui China Chuzhou University Anhui China

ISBN: (纸本)9781510680425

remote sensing image scene categorization is a crucial process that involves assigning semantic labels to images based on their content,which has wide-ranging applications in a number of *** inherent potential of deep neural networks to learn attractive features has substantially sped up progress in the field, resulting in considerable breakthroughs in remote sensing and scene classification powered by deep ***, the existing literature lacks a comprehensive evaluation of research findings in deep learning for remote sensing image scene categorization, despite the notable advancements in this *** CNN has shown remarkable efficacy in extracting image features, ranging there are persistent challenges that hinder its optimal performance in the domain of categorization of images from data collected from remote *** intrinsic attributes linked to remote sensing image data, such as intraclass variability and inter-class similarities, result in a notable decrease in the classification accuracy of the ***, the discernible impact of noise and interference further exacerbates limitations in CNN performance within this specific *** response to these challenges, this study introduces an innovative classification methodology that seamlessly integrates a triplet network and ***, the paper systematically conducts a rigorous comparative assessment, evaluating the performance of diverse image classification models across various remote sensing *** holistic approach aims to contribute to the evolving landscape of remote sensing image scene classification by addressing existing gaps and advancing our understanding of effective model architectures. © 2024 SPIE.

关键词： Deep neural networks

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Computational methods to retrieve soil moisture using remote sensing data: A review 7

Computational methods to retrieve soil moisture using remote...

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IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Inoubli, Raja Constantino-Recillas, Daniel Enrique Monsivais-Huertero, Alejandro Farah, Lilia Bennaceur Farah, Imed Riadh Natl Sch Comp Sci Labratoire RIADI Manouba 1001 Tunisia Tecnol Nacl Mexico TES Ecatepec Mexico City DF Mexico Inst Politecn Nacl ESIME Ticoman Mexico City DF Mexico Preparatory Inst Engn Studies El Manar LTSIRS Lab Manar Tunisia Natl Inst Engn Sci Tunis Manar Tunisia

ISBN: (纸本)9798350351491;9798350351484

Recently, soil moisture (SM) retrieval has become the first interest of the scientific community due to the increasing demand for water resources in several fields, especially the agricultural sector. A lack of such resources has been detected along with the increasing climate changes and disasters occurring such as drought. It has become very essential to search for efficient approaches and data sources that can serve effectively the SM retrieval goals. This paper is a literature review set to identify the strengths and weaknesses of the most relevant works in the area of SM retrieval and remote sensing (RS) published from 2017 to 2024. Summarizing the selected studies serves to detect the relevant information that tackles the SM retrieval challenges. Thus, creating new horizons for new research. One of the main fields of interest is SM retrieval over agriculture based on the synergistic use of Sentinel-1 (S-1) and Sentinel-2 (S-2) images. High accuracy has been achieved by combining different approaches such as backscattering models and Machine Learning (ML) algorithms (e.g., AIEM and NN algorithms). The application of such a combination is highly recommended due to its promising potential.

关键词： Soil moisture arid and semi-arid regions backscattering models Machine learning Change detection hybrid approaches remote sensing retrieval

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Open-Set remote sensing Object Detection Using Edge Information Extraction 2

Open-Set Remote Sensing Object Detection Using Edge Informat...

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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Li, Xiaozhe Dang, Sihang Sun, Yifei Jiang, Xiaoyue Gui, Shuliang Feng, Xiaoyi Northwestern Polytechnical University School of Electronics and Information Xi'an China Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing China

ISBN: (纸本)9798331515669

Typically, remote sensing object detection is limited to a closed-set detection environment. A problem is that when detecting the new untrained but valuable objects, they are incorrectly classified as known classes or background. In this paper, a method is proposed for open-set detection of remote sensing objects, which assigns robust pseudo-labels to unknown classes and trains the network to recognize new classes. The main idea is to combine the feature information of remote sensing objects and select regions in the image that are likely to contain unknown classes to form pseudo-labels. Through supervised learning, the network can distinguish and detect both known and unknown classes. remote sensing objects are observed from an Earth observation perspective, with rich edge information. Pseudo-labels for unknown classes are obtained using image convolution features and object edge information. Experimental results show that this method outperforms existing methods in open-set target detection for remote sensing images. © 2024 IEEE.

关键词： Optical remote sensing

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Algorithmic Research on Hydrogeological Comprehensive Monitoring Using Multi-Source remote sensing Data 24

Algorithmic Research on Hydrogeological Comprehensive Monito...

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International conference on Machine Intelligence and Digital Applications (MIDA)

作者： Yuan, Yuan Shandong Prov Bur Geol & Mineral Resources Jinan 250014 Shandong Peoples R China Shandong Engn Res Ctr Environm Protect & Remediat Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9798400718144

In response to the difficulties in obtaining data and high-error data in current hydrogeological surveys, this article uses high-resolution satellite image processing to capture wastewater characteristics based on the principle of remote sensing reflectance. The automatic threshold selection algorithm determines the optimal threshold for extracting wastewater information and establishes a model for identifying wastewater, achieving the purpose of better identification of water quality. This article uses drones to capture geological images for processing. Based on two-dimensional wavelet transform, remote sensing images are divided into four sub bands and filtered. The four sub bands can be input into the residual network learning algorithm to obtain high-quality four new sub bands, and finally transformed into a complete high-quality remote sensing geological image through inverse two-dimensional wavelet transform, achieving the purpose of better identification of water quality. The results show that the wastewater recognition model can accurately identify wastewater according to the characteristics that the reflectance of wastewater water in blue. Green and red wavelengths is lower than that of normal water, while the reflectance in infrared wavelengths is higher than that of normal water. The peak signal-to-noise ratio and mean square error of the residual learning network algorithm using two-dimensional wavelet transform for geological remote sensing images are 28.12 and 0.051, which are the best compared with the other two methods, indicating that the image quality after processing is high.

关键词： Hydrogeological Study Multi-Source remote sensing Automatic Threshold Selection Two-dimensional Wavelet Transform Residual Network Learning

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