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2024 International Conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Wu, Wangsheng College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

To improve the application efficiency of RGB remote sensing images in agricultural land resource surveys, a cultivated land segmentation algorithm based on kernel space non-uniform regularization classification and improved graph cut was proposed. Firstly, extracting texture and color features of remote sensing images using Local Binary pattern algorithm (LBP), Gabor filters, and RGB, HSV color space, respectively. Next, introducing a kernel method to map data from low-dimension to high-dimension, and construct a kernel space-based non-uniform regularization sparse representation model to classify and segment images in pixel level. Finally, an innovative graph cut algorithm is enhanced by incorporating a Gaussian distribution to redefine the penalty term for homogeneous regions and introducing a new color gradient measure to define the penalty term for boundaries. This approach effectively removes scatter and restricts the segmentation boundary. The average classification accuracy and average F1 score of the classifier proposed in this paper are about 2% and 3% higher than those of recent regularized subspace classifiers, respectively. Compared with the Graph cut algorithm, the proposed improved graph cut algorithm has an average mIoU improvement of about 9%. The average accuracy of the whole segmentation algorithm is 95.43%, and the average mIoU is 88.56%. Compared with the comparison algorithm, the proposed algorithm has higher segmentation accuracy, which proves that the proposed algorithm can adapt to the cultivated land segmentation scene of remote sensing images and is effective. © 2024 SPIE.

关键词： RGB color model

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SPECTRAL-AWARE DEEP NETWORKS FOR OBJECT DETECTION IN HYPERSPECTRAL imageS WITH CLOUD INTERFERENCE 24

SPECTRAL-AWARE DEEP NETWORKS FOR OBJECT DETECTION IN HYPERSP...

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1st International Conference on image processing Machine Learning and pattern recognition

作者： Zhang, Ying Zeng, Xia Zhang, Hongtao Xu, Meng Shenzhen Univ Shenzhen Peoples R China Beijing Inst Astronaut Syst Engn Beijing Peoples R China

ISBN: (纸本)9798400707032

Hyperspectral object detection (HOD) aims to identify and locate multiple objects in a scene using hyperspectral images (HSIs). While much research has focused on hyperspectral target detection (HTD) at the pixel level, HOD remains underexplored. Traditional HTD methods rely heavily on prior spectral information of the target and simple pixel neighborhood relationships, leading to accuracy issues when targets are occluded. Inspired by advances in RGB image detection, we propose a compact and efficient cloud-robust hyperspectral object detection network (CR-HODNet) using 3D convolution to extract spatial and spectral features jointly. We further enhance these features with channel and spatial attention mechanisms and address cloud occlusion challenges using transformer-based multi-head attention. Our method is validated on real airborne hyperspectral images with synthetic cloud occlusion, showing robust performance in challenging scenarios.

关键词： Hyperspectral image Object detection Cloud interference remote sensing

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Rethinking Transformers Pre-training for Multi-Spectral Satellite imagery

Rethinking Transformers Pre-training for Multi-Spectral Sate...

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Noman, Mubashir Naseer, Muzammal Cholakkal, Hisham Anwar, Rao Muhammad Khan, Salman Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. Different from standard natural image datasets, remote sensing data is acquired from various sensor technologies and exhibit diverse range of scale variations as well as modalities. Existing satellite image pre-training methods either ignore the scale information present in the remote sensing imagery or restrict themselves to use only a single type of data modality. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities. Our proposed approach, named SatMAE++, performs multiscale pre-training and utilizes convolution based upsampling blocks to reconstruct the image at higher scales making it extensible to include more scales. Compared to existing works, the proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery. Extensive experiments on six datasets reveal the merits of proposed contributions, leading to state-of-the-art performance on all datasets. SatMAE++ achieves mean average precision (mAP) gain of 2.5% for multi-label classification task on BigEarthNet dataset. Our code and pre-trained models are available at https://***/techmn/satmae_pp.

关键词： multi-scale mask auto encoder multi-spectral imagery remote sensing scene classification self-supervised learning Vision Transformers

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RST-DeepLabv3+: Multi-Scale Attention for Tailings Pond Identification with DeepLab

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remote sensing 2025年第3期17卷 411-411页

作者： Feng, Xiangrui Wei, Caiyong Xue, Xiaojing Zhang, Qian Liu, Xiangnan China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Ningxia Inst Remote Sensing Survey High Resolut Satellite Remote Sensing Applicat Dep Yinchuan 750021 Peoples R China

Tailing ponds are used to store tailings or industrial waste discharged after beneficiation. Identifying these ponds in advance can help prevent pollution incidents and reduce their harmful impacts on ecosystems. Tailing ponds are traditionally identified via manual inspection, which is time-consuming and labor-intensive. Therefore, tailing pond identification based on computer vision is of practical significance for environmental protection and safety. In the context of identifying tailings ponds in remote sensing, a significant challenge arises due to high-resolution images, which capture extensive feature details-such as shape, location, and texture-complicated by the mixing of tailings with other waste materials. This results in substantial intra-class variance and limited inter-class variance, making accurate recognition more difficult. Therefore, to monitor tailing ponds, this study utilized an improved version of DeepLabv3+, which is a widely recognized deep learning model for semantic segmentation. We introduced the multi-scale attention modules, ResNeSt and SENet, into the DeepLabv3+ encoder. The split-attention module in ResNeSt captures multi-scale information when processing multiple sets of feature maps, while the SENet module focuses on channel attention, improving the model's ability to distinguish tailings ponds from other materials in images. Additionally, the tailing pond semantic segmentation dataset NX-TPSet was established based on the Gauge-Fractional-6 image. The ablation experiments show that the recognition accuracy (intersection and integration ratio, IOU) of the RST-DeepLabV3+ model was improved by 1.19% to 93.48% over DeepLabV3+.The multi-attention module enables the model to integrate multi-scale features more effectively, which not only improves segmentation accuracy but also directly contributes to more reliable and efficient monitoring of tailings ponds. The proposed approach achieves top performance on two benchmark datasets, NX-TPSe

关键词： tailings pond identification Convolutional Neural Network (CNN) multi-scale attention semantic segmentation

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Automatic recognition and detection of building targets in urban remote sensing images using an improved regional convolutional neural network algorithm

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COGNITIVE COMPUTATION AND SYSTEMS 2023年第2期5卷 132-137页

作者： Lin, Sida Huaqiao Univ Sch Architecture Xiamen Fujian Peoples R China

The accuracy of regional convolutional neural network (R-CNN) algorithms on image recognition detection remains to be improved. The authors optimised the Mask R-CNN algorithm and tested it through experiments on the automatic recognition of building targets in urban remote sensing images. It was found that the improved Mask R-CNN algorithm recognised more complete building targets and clearer edges than the original algorithm with a precision of 95.75%, a recall rate of 96.28% and a mean average precision (mAP) of 0.9403, and it also reduced the detection time per image to 0.264 s, all of which were better than other R-CNN algorithms. The ablation experiments showed that compared with the original Mask R-CNN algorithm, the improvement in the mAP of the Mask R-CNN algorithms with an improved feature pyramid network and an improved non-maximum suppression (NMS) algorithm was 0.0206 and 0.0119, respectively, while the improvement in the mAP of the improved Mask R-CNN algorithm was 0.0376. The two improvement methods adopted for the Mask R-CNN algorithm were proved to be feasible and can effectively improve the automatic recognition and detection accuracy and efficiency of building targets in urban remote sensing images.

关键词： artificial intelligence image processing

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Atmospheric Correction of High Resolution remote sensing images with Automatic Data Acquisition by Network 24

Atmospheric Correction of High Resolution Remote Sensing Ima...

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1st International Conference on image processing Machine Learning and pattern recognition

作者： Wan, Xingyu Chen, Xingfeng Li, Kaitao Wang, Yanping Xiao, Qian Wan, Wei Liu, Shumin Zhao, Limin Jiangxi Univ Sci & Technol Sch Software Engn Ganzhou Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Space Engn Univ Sch Space Informat Beijing Peoples R China Inst Disaster Prevent Sanhe Peoples R China Space Star Technol Co Ltd Beijing Peoples R China Peking Univ Inst Remote Sensing & GIS Sch Earth & Space Sci Beijing Peoples R China

ISBN: (纸本)9798400707032

Atmospheric parameters are necessary inputs for atmospheric correction, but obtaining these parameters is difficult. To address this challenge, a solution for atmospheric parameter acquisition based on NNAeroG and networked automatic matching was proposed. This solution, combined with QUAAC, enables the atmospheric correction of GF images, thereby achieving full process automation of atmospheric correction. This scheme effectively simplifies the tedious process of obtaining AOD in existing methods and greatly improves the efficiency of atmospheric correction. The atmospheric parameters provided by this program can support multiple atmospheric correction methods, reduce labor-intensive operations, and offer efficient tools for large-scale atmospheric radiation production and research.

关键词： Atmospheric correction Automated program Aerosol optical depth Geostationary satellite

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GANFlow: A Hybrid Model for SAR image Target Open-Set recognition Based on GAN and the Flow-Based Module

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 7083-7099页

作者： Qin, Jikai Han, Jiusheng Liu, Zheng Ran, Lei Xie, Rong Yeo, Tat-Soon Air Force Engn Univ Xian 710051 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Natl Univ Singapore Dept Elect Engn Singapore 119077 Singapore

Most synthetic aperture radar (SAR) automatic target recognition (ATR) methods can achieve good recognition results only under the closed-set assumption. However, in practical applications, ATR models are often exposed to open environments, the general closed-set method may misclassify unknown categories as known categories, which is not reasonable. To tackle this issue, this article proposes an end-to-end hybrid model for SAR image open-set recognition (OSR), named GANFlow, which combines a generative adversarial network (GAN) with a flow-based module. The GANFlow achieves accurate classification of known categories and effective rejection of unknown categories. In this model, a classifiable convolution GAN is first designed to complete the training of the feature extraction module and classifier. Through adversarial training, the generated images enrich the training samples, which improves the ability of feature extraction and classification of the discriminator. Then, to find the difference in the probability density distribution of the extracted features, a flow-based module is adopted. Also, the features avoid interference from irrelevant background information in SAR images. Furthermore, by establishing an appropriate threshold, unknown categories can be efficiently rejected. Finally, the outputs of the classifier and the flow-based module are combined to complete the OSR of the SAR image target. The experimental results on the MSTAR and OpenSARShip public-measured datasets verify the robustness and generalization of the proposed method.

关键词： Radar polarimetry Training Feature extraction Generative adversarial networks Target recognition image recognition Data models Accuracy Synthetic aperture radar Support vector machines Automatic target recognition (ATR) flow generative adversarial network (GAN) open-set recognition (OSR) synthetic aperture radar (SAR)

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Railroad switch machine condition monitoring technology based on image recognition 12

Railroad switch machine condition monitoring technology base...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhang, Yan Xiao, Jie School of Railway Signaling and Engineering Wuhan Railway Vocational College of Technology Wuhan China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

The size of the gap in the switch machine can reflect the working status of the switch machine and is related to the driving safety and driving efficiency of railway *** the traditional switch machine inspection and maintenance process, manual measurement is mainly used to complete the inspection of the gap size. This article discusses a gap monitoring system based on image recognition. © 2024 SPIE.

关键词： image recognition

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International Conference on image, Signal processing, and pattern recognition, ISPP 2024

International Conference on Image, Signal Processing, and Pa...

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2024 International Conference on image, Signal processing, and pattern recognition, ISPP 2024

ISBN: (纸本)9781510680425

The proceedings contain 263 papers. The topics discussed include: improved YOLOv5's remote sensing image detection algorithm;wavelet transform based polarized image fusion detection of underwater targets;CLIP-driven hierarchical fusion for referring image segmentation;research on continuous monitoring of subdural hematoma based on optical image mapping feature extraction method;generalized image denoising based on MLP denoiser and diffusion model;CAM: consistency adversarial model for image generation with high-frequency image details;DBE-net: double-level boundary enhanced network for temporomandibular joint CBCT images segmentation;an enhanced feature matching multi-temporal port remote sensing image registration network E-SuperGlue;and application of 3D laser scanning and tilt photography technology in digital landscape surveying and mapping.

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SCD-SAM: Adapting Segment Anything Model for Semantic Change Detection in remote sensing imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2024年 62卷 1页

作者： Mei, Liye Ye, Zhaoyi Xu, Chuan Wang, Hongzhu Wang, Ying Lei, Cheng Yang, Wei Li, Yansheng Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Univ Inst Technol Sci Wuhan 430072 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Univ New South Wales Fac Engn Sydney NSW 310020 Australia Wuchang Shouyi Univ Sch Informat Sci & Engn Wuhan 430032 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430072 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China

Semantic change detection (SCD) has gradually emerged as a prominent research focus in remote sensing image processing due to its critical role in Earth observation applications. In view of its powerful semantic-driven feature extraction capability, the segment anything model (SAM) has demonstrated its suitability across various visual scenes. However, it suffers from significant performance degradation when confronted with remote sensing images, especially those containing various ground objects that possess significant interclass similarity and substantial intraclass variations. To address the above issues, we propose SCD-SAM, aiming to leverage the potent visual recognition capabilities of SAM for enhanced accuracy and robustness in SCD. Specifically, we introduce a contextual semantic change-aware dual encoder that combines MobileSAM and CNN to extract progressive semantic change features in parallel and inject local features into the MobileSAM encoder through depth feature interaction (DFI) to compensate for the Transformer's limitations in perceiving local semantic details. In addition, in order to utilize the strong visual feature extraction capability of MobileSAM in remote sensing images, we propose a semantic adaptor that aggregates semantic-oriented information about changing objects. To better integrate the extracted contextual semantic information, we devise a progressive feature aggregation dual decoder that aggregates binary change features and semantic change features, respectively, alleviating the semantic gap across different scales. The quantitative and visual results show that SCD-SAM outperforms the state-of-the-art SCD methods on publicly open SCD datasets (e.g., SECOND-CD and Landsat-CD). The code will be available at: https://***/yzygit1230/SCD-SAM.

关键词： Semantics Feature extraction remote sensing image segmentation Decoding Adaptation models Transformers Progressive feature aggregation segment anything model (SAM) semantic adaptor semantic change detection (SCD)

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