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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

共 3605 条记录，以下是21-30 订阅

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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...

引用

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...

引用

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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Koopman Ensembles for Probabilistic Time Series Forecasting 32

Koopman Ensembles for Probabilistic Time Series Forecasting

引用

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...

引用

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

Algorithmic Research on Hydrogeological Comprehensive Monito...

引用

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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M2F2Net: Multi-stage Mixed Feature Fusion Network For remote sensing Change Detection

M2F2Net: Multi-stage Mixed Feature Fusion Network For Remote...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Gu, Binhao Song, Lei Gu, Binjie Kong, Youyong Southeast University Nanjing China Jiangnan University Wuxi China

ISBN: (纸本)9798350368741

remote sensing image change detection (CD) seeks to analyze and discern changes in surface objects through the use of multi-temporal remote sensing imagery. However, as image resolution advances, existing methods often fall short in capturing comprehensive visual feature representations, and their networks are prone to spatial degradation. This results in incomplete boundary detection and difficulties in identifying subtle changes. To overcome these challenges, this paper introduces a Siamese U-Net architecture incorporating Multistage Mixed Feature Fusion (M2F2Net). The proposed model leverages a convolutional neural network (CNN) as the main encoder for local feature extraction, while employing a Transformer-based auxiliary encoder to capture global features. Furthermore, we introduce a Feature Fusion Module (FFM) to facilitate the efficient integration of local and global information. Additionally, we propose a novel convolutional unit and a Spatial Attention Module (SAM) designed to enhance the extraction of image features. Experimental results confirm that the proposed approach delivers substantial improvements across multiple evaluation criteria, while offering a superior accuracy compared to existing state-of-the-art change detection methods. © 2025 IEEE.

关键词： Change detection (CD) Deep learning Multi scale remote sensing images Self attention mechanism

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

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

引用

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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SAR Ship Classification based on Second-order Synchrosqueezing Transform and HOG Feature

SAR Ship Classification based on Second-order Synchrosqueezi...

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9th International conference on signal and image processing (ICSIP)

作者： Li, Dongrui Zhao, Zhichun Shenzhen MSU BIT Univ Fac Engn Shenzhen Peoples R China

ISBN: (纸本)9798350350920

Accurate classification and identification of vessels in remote sensing satellite imagery is critical for ocean monitoring and resource management. The ability to extract information from remote-sensing data is of paramount importance. To exploit the non-stationary characteristics of synthetic aperture radar (SAR) target, a comprehensive SAR ship recognition framework is designed by combing the second-order synchrosqueezing transform (SST), an effective non-stationary signal processing tool, with the histogram of oriented gradient (HOG) feature in this paper. Firstly, the second-order SST is performed on SAR images to describe the non-stationary characteristics of ships at different times and frequencies. Secondly, HOG features are utilized to effectively extract the non-stationary information of SAR ships and provide more discriminative input for the deep learning network. Then, the optimal ResNet model is selected as the convolutional neural network (CNN) classifier to automatically fuse the non-stationary features and abstract features of SAR ships. Experiments on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) show that the proposed method achieves accurate classification and outperforms the state-of-the-art (SOTA) CNN-based methods in terms of robustness and generalization ability. The positive effect of non-stationary characteristics on SAR ship classification is verified.

关键词： Synthetic aperture radar (SAR) ship classification non-stationary signal processing time-frequency analysis (TFA) histogram of oriented gradient (HOG) convolutional neural network (CNN)

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Target point alignment with Gaussian mixture models 16

Target point alignment with Gaussian mixture models

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16th International conference on signal processing Systems, ICSPS 2024

作者： Li, Chuchen Zhao, Bingqing Tang, Shanjun Li, Xing Liao, Xin Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

Under the remote sensing perception scenario, the sensor field of view alignment is the key step for global information acquisition. However, the model can only utilize position information for alignment, as the remote sensing image has limited resolution and the size of targets is relatively small. And because of inevitable noise and complex background information, false alarms are difficult to eliminate. Moreover, the number of targets that a remote sensor can capture can be scarce compared with the general point registration task. With all these problems, precisely aligning remote sensing images that contain similar targets is extremely difficult. In this paper, we propose Gaussian mixture models with shape and false alarm sensitive strategy to tackle the limited resolution remote sensing image alignment task. We first apply shape context as a structure description to add intrinsic information to our model. Then we introduce a false alarm sensitive strategy to refine the information from the shape context to further enhance the alignment ability of the model. We simulate different target structures and conditions as our evaluation dataset. Thorough experimental results show the effectiveness of our proposed method. © 2025 SPIE.

关键词： Optical remote sensing

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