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

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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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Full-scale assessment of pansharpening: why literature indexes may give contradictory results and how to avoid such an inconvenience 29

Full-scale assessment of pansharpening: why literature index...

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conference on image and signal processing for remote sensing xxix

作者： Alparone, Luciano Garzelli, Andrea Lolli, Simone Zoppetti, Claudia Univ Florence Dept Informat Engn I-50139 Florence Italy Univ Siena Dept Informat Engn & Math I-53100 Siena Italy Tito Scalo Consiglio Nazl Ric CCNR IMAA I-85050 Potenza Italy Univ Politecn Cataluna CommSensLab Barcelona 08034 Spain

ISBN: (纸本)9781510666955;9781510666962

In this paper, we wish to explain the contradiction of quality assessments of pansharpening carried out at full and reduced spatial scales. It seems that at full scale, methods based on component substitution (CS) are quantitatively poorer than the other methods, but this depends on the intrinsic space varying misregistration between the two datasets. At reduced scale, the local shifts are divided by the MS-to-Pan scale ratio and thus they tend to vanish. The problem of full-scale quality indexes is that they were originally validated on aerial multispectral (MS) data, with synthetic panchromatic (Pan) and thus total absence of misregistration. In the presence of local misregistration due to inaccurate information of the height of the imaged surface, CS methods locally align the lowpass MS components towards the sharpening Pan, thereby preserving the geometry of the scene;all the other methods produce fading contours because of shifts. The favorable property of CS, however, impacts against the (spectral) consistency property of Wald's protocol, developed when the misalignments between MS and Pan was a small fraction of the pixel size, and hence negligible. In this perspective, methods that do not shift the original MS information are better, even though the visual quality of fading contours is worse. After exposing and explaining the contradiction between full- and reduced-scale assessments, we perform an in-depth analysis of the spectral and spatial consistency indexes of three widespread full-scale protocols: QNR, KQNR and HQNR. We investigate the robustness to shifts of all consistency indexes and propose to couple the spectral index and the spatial index that are least sensitive to shifts. In this way, the ranking of methods of reduced-scale assessments is preserved in full-scale assessments.

关键词： Full-scale assessment image fusion pansharpening satellite acquisition geometry very high resolution (VHR) multispectral (MS) images

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

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

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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...

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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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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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remote sensing image super-resolution reconstruction using depth-dense residual and sub-pixel convolutional model

Remote sensing image super-resolution reconstruction using d...

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

作者： Liu, Haoguang Fu, Yuwen Wang, Zhoujie Chinese Flight Test Establishment Xian China

ISBN: (纸本)9781510680425

Existing super-resolution reconstruction algorithms for remote sensing images often struggle to fully extract and utilize features in complex scenes, and the reconstruction results are not optimal due to the influence of *** propose a reconstruction network model that combines deep dense residual module and sub-pixel *** model connects multiple dense residual modules through recursive linking and introduces a channel attention mechanism to extract multiscale features from the *** the feature reconstruction process, a sub-pixel convolution structure is introduced to reduce the impact of noise and enhance reconstruction *** model is tested on the UC Merced Land Use public dataset, and the results demonstrate that the reconstruction results of our proposed model algorithm have better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at different scales compared to the current mainstream reconstruction algorithm *** is proven that the proposed model can significantly improve the reconstruction quality through comparative analysis experiment, meeting the needs for high-resolution remote sensing image processing. © 2024 SPIE.

关键词： image reconstruction

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A Coarse-to-Fine Change Detection Method for remote sensing Sparse Cultivated Land

A Coarse-to-Fine Change Detection Method for Remote Sensing ...

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2024 Asia Pacific signal and Information processing Association Annual Summit and conference

作者： Hu, Yuan Zhang, Yifan Ma, Mingyang Mei, Shaohui Northwestern Polytech Univ Xian 710072 Peoples R China

ISBN: (纸本)9798350367331;9798350367348

remote sensing (RS) images contain rich geographic information. For specific application scenarios like cultivated land, it is necessary to select areas of interest to reduce data scale and focus on detailed features. In this article, an innovative coarse-to-fine change detection method (CFCD) for sparse cultivated land is proposed to address these problems. Coarse screening module (CSM) first removes irrelevant low-difference image pairs, and then fine detection module (FDM) accurately locate change areas in remaining images. Experimental results show that two coarse screening methods can take out many disturbed images, and provide strong support for subsequent fine detection methods to achieve performance improvement.

关键词： Change detection

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