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Hyperspectral and LiDAR Data Classification Based on Structural Optimization Transmission

IEEE TRANSACTIONS ON CYBERNETICS 2023年第5期53卷 3153-3164页

作者： Zhang, Mengmeng Li, Wei Zhang, Yuxiang Tao, Ran Du, Qian Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab Fract Signals & Syst Beijing 100081 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

With the development of the sensor technology, complementary data of different sources can be easily obtained for various applications. Despite the availability of adequate multisource observation data, for example, hyperspectral image (HSI) and light detection and ranging (LiDAR) data, existing methods may lack effective processing on structural information transmission and physical properties alignment, weakening the complementary ability of multiple sources in the collaborative classification task. The complementary information collaboration manner and the redundancy exclusion operator need to be redesigned for strengthening the semantic relatedness of multisources. As a remedy, we propose a structural optimization transmission framework, namely, structural optimization transmission network (SOT-Net), for collaborative land-cover classification of HSI and LiDAR data. Specifically, the SOT-Net is developed with three key modules: 1) cross-attention module;2) dual-modes propagation module;and 3) dynamic structure optimization module. Based on above designs, SOT-Net can take full advantage of the reflectance-specific information of HSI and the detailed edge (structure) representations of multisource data. The inferred transmission plan, which integrates a self-alignment regularizer into the classification task, enhances the robustness of the feature extraction and classification process. Experiments show consistent outperformance of SOT-Net over baselines across three benchmark remote sensing datasets, and the results also demonstrate that the proposed framework can yield satisfying classification result even with small-size training samples.

关键词： Laser radar Feature extraction Optimization Indexes Hyperspectral imaging Collaboration Task analysis Collaborative classification convolutional neural network (CNN) deep learning hyperspectral image (HSI) light detection and ranging (LiDAR) data pattern recognition remote sensing

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Unveiling the Influence of image Super-Resolution on Aerial Scene Classification 26th

Unveiling the Influence of Image Super-Resolution on Aerial ...

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26th Iberoamerican Congress on pattern recognition (CIARP)

作者： Ibrahim, Mohamed Ramzy Benavente, Robert Ponsa, Daniel Lumbreras, Felipe Arab Acad Sci Technol & Maritime Transport Dept Comp Engn Alexandria Egypt Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain Univ Autonoma Barcelona Dept Comp Sci Barcelona Spain

ISBN: (纸本)9783031490170;9783031490187

Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications.

关键词： Super-resolution Scene classification Deep learning Aerial images remote sensing

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remote sensing Object Detection Based on Improved Detection Transformer 3

Remote Sensing Object Detection Based on Improved Detection ...

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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Hu, Yuanzhong Hu, Po Wang, Haihui Wang, Yuzhuo School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331506582

Nowadays, the accuracy of remote sensing image target recognition has greatly improved. It enables humans to efficiently monitor crop growth, track climate changes and natural disasters, and even provide critical strategic intelligence for national defense. However, due to the challenges posed by the large number of objects and the immense size of remote sensing images, there is still considerable room for improvement in recognition accuracy and efficiency. Detection Transformer (DETR) framework utilizes the Transformer architecture, change the object detection task to a set prediction problem, thus simplifying the traditional post-processing workflow. Through its attention mechanisms, DETR effectively captures object features in intricate scenes by modeling global contextual information. Nonetheless, there is still potential for improvement in extracting fine-grained details. In this study, three modifications are proposed to the DETR framework to boost remote sensing object detection accuracy. First, a multi-scale feature extraction approach is employed to capture deeper and more detailed image features. Second, a bidirectional learning path is introduced to enhance the contextual relationships between image features. Experimental results demonstrate that our proposed method has achieved strong competitiveness compared to other approaches © 2024 IEEE.

关键词： Optical remote sensing

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GLA-ViT: A Global Contextual Attention Based for remote sensing image Scene Classification 22

GLA-ViT: A Global Contextual Attention Based for Remote Sens...

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22nd IEEE International Symposium on Parallel and Distributed processing with Applications, ISPA 2024

作者： Yuan, Hao Liu, Kun Peng, Qinghao Zou, Benli Wang, Can Li, Meishu University of Jinan School of Information Science and Engineering Jinan China Shandong Center of Data and Application China High-Resolution Earth Observation System Jinan China

ISBN: (纸本)9798331509712

remote sensing image Scene Classification (RSISC) is a critical area of research in remote sensing that is dedicated to categorizing remote sensing images into specific semantic classes based on their content. Despite the Convolutional Neural Networks' (CNNs) effectiveness in feature extraction and classification, they face challenges in exploring contextual information necessary for a comprehensive understanding of remote sensing scenes. To address this limitation, a novel model based on the Transformer architecture has attracted considerable attention from researchers. The Transformer model has shown promise in extracting contextual information within remote sensing (RS) scenes. However, the original Transformer model's performance in RS scene classification has been subpar due to the diverse content types and scales present in remote sensing scenes. To overcome these challenges, a context-based approach for remote sensing image scene classification called GLA-ViT is proposed. The GLA-ViT model integrates local and global self-attention modules to enhance its ability to capture intricate details and contextual information in remote sensing images, while also maintaining computational efficiency. Additionally, GLA-ViT incorporates an improved MFused-MBConv module to enhance the model's recognition capabilities in complex scenes. Experimental results on well-known datasets such as AID, UCM, and NWPU-RESISC45 demonstrate the superior performance of GLA-ViT in remote sensing image scene classification related tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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HyLiOSR: Staged Progressive Learning for Joint Open-Set recognition of Hyperspectral and LiDAR Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Xi, Bobo Cai, Mingshuo Li, Jiaojiao Wang, Zhengjue Feng, Shou Li, Yunsong Chanussot, Jocelyn Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510000 Guangdong Peoples R China Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China Xidian Univ Sch Artificial Intelligence State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Harbin Engn Univ Sch Informat & Commun Engn Harbin Peoples R China Univ Grenoble Alpes Inria CNRS Grenoble INPLJK F-38000 Grenoble France

The joint classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data have seen significant advancements in recent research. However, it would be more practical if we could simultaneously detect the unknown classes in a more realistic open-set scenario. In this article, we introduce a novel open-set recognition (OSR) method for HSI and LiDAR data, termed HyLiOSR, which devises a staged progressive learning strategy to effectively bridge the gap between closed-set and open-set feature distributions within an autoencoder framework. Specifically, for the first stage, the reconstruction-based network is dedicated to accurately modeling each known category by learning multiple Gaussian prototypes, which facilitates OSR by disentangling the distribution of known classes. In the second stage, we actively synthesize samples of unknown classes during the feature extraction phase and create a virtual unknown classifier, enabling the network to effectively differentiate between known and unknown class samples. This approach establishes a distinct separation between known and unknown classes in the latent feature space, thereby enhancing the capability of the frameworks to distinguish between them. Comprehensive experiments conducted on three benchmark datasets demonstrate that the proposed HyLiOSR outperforms existing state-of-the-art methods. The source code will be accessible at https://***/B-Xi/TGRS_2025_HyLiOSR.

关键词： image reconstruction Prototypes Laser radar Feature extraction Autoencoders Training Electronic mail Vectors Transformers image recognition Autoencoder Gaussian prototypes hyperspectral image (HSI) light detection and ranging (LiDAR) open-set recognition (OSR)

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Accurate Laser Line Extraction Algorithm Based on Morphological Features under Strong Interference 12

Accurate Laser Line Extraction Algorithm Based on Morphologi...

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

作者： Wu, Yuanzhe Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In outdoor complex lighting environments, laser stripe extraction becomes challenging due to the possibility of strong exposures in the external environment as well as reflective interference on the road surface. In order to overcome these problems, this paper proposes a multi-feature structured light extraction algorithm based on seed neighborhoods, which utilizes multiple seed points and comprehensively considers the laser stripe features, especially the morphological features of the optical stripes in their surrounding neighborhoods, so as to obtain the approximate distribution location of the real laser stripe. Finally, the laser streak extraction is performed by the gradient difference of the optical streak boundary as well as its own brightness. The experimental results show that the method is still effective in extracting laser lines even in high exposure environments where there are interferences such as reflections on the road surface. © 2024 SPIE.

关键词： Luminance

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A temporal-spatial feature coordination network for change detection of VHR images 3

A temporal-spatial feature coordination network for change d...

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3rd International Conference on Geographic Information and remote sensing Technology, GIRST 2024

作者： Zeng, Yuxian Zhang, Xinxin College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510689077

Deep learning techniques have significantly improved the accuracy and efficiency of change detection of very high resolution (VHR) images. However, many current models ignore the inherent heterogeneity of bi-temporal remote sensing images, thus making it difficult to distinguish between target changes and background variations. To address this problem, we propose a deep learning (DL) framework, namely temporal-spatial feature coordination network (TSFCN), to improve the accuracy of detection by enhancing the differentiation of change pixel features and improving the extraction and processing of change information. Further comparison and ablation experiments on the SVCD dataset show that the proposed model, compared with other models, improves the F1 scores and IoU by 2.09% and 3.81%, respectively. The results demonstrate that our model can achieve better accuracy and performance improvement. © 2025 SPIE.

关键词： Change detection

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Study on multi-annual and simultaneous satellite-ground remote sensing retrieval of water transparency (Secchi disk depth) in Poyang lake

Study on multi-annual and simultaneous satellite-ground remo...

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

作者： Zheng, Pu Xu, Xin-Fa Xu, Xiao-Hua Huang, Ping Zhou, Xin-Wen Jiang xi Academy of Water Science and Engineering Nanchang330029 China Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin 330029 China

ISBN: (纸本)9781510680425

Poyang Lake is the largest freshwater lake in China. It is located in the middle and lower reaches of the Yangtze River in the north-central Jiangxi Province. As the largest Lake which is connecting the Yangtze River, the water ecological security of Poyang Lake affects the entire Yangtze River basin. Water transparency(SDD) is an important indicator of water quality, so it is of great significance to carry out routine monitoring of SDD using remote sensing methods. This study takes Poyang Lake as the experimental area, and uses the SDD as the inversion index to characterize water quality. Based on observation data from 2015 to 2020 and Landsat series images, the inversion of SDD model suitable for summer Poyang Lake was constructed by analyzing and comparing the correlation coefficients between different wave bands and the SDD under the condition of standardized modeling phase and "star-ground" collaborative matching. After verification by four sets of independent data, the mean R2 is 0.8621, the mean RSME is 15.37cm, and the mean MAPE is 23.6%. It is believed that the method used in this study can be well applied to the inversion of SDD in the Poyang Lake area, and also provides a new reference method for monitoring the transparency of inland water bodies under long-term observation conditions. © 2024 SPIE.

关键词： Lakes

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remote sensing image Object Detection Method with Feature Denoising Fusion Module 7

Remote Sensing Image Object Detection Method with Feature De...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Chen, Penghui Li, Qishen Li, Qiufeng Wu, Zhongyu Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University School of Software Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350339161

remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models. © 2024 IEEE.

关键词： Object detection

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Design of Cloud Platform for Efficient remote sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Huang, Yan Zhang, Weimeng Xi'an Traffic Engineering Institute Shaanxi Xi'an China Xi'an Technical School Shaanxi Xi'an China

ISBN: (纸本)9798350315455

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management. © 2023 IEEE.

关键词： linux shell open stack remote sensing data management spark

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