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Land scene classification for remote sensing images with an improved capsule network

JOURNAL OF APPLIED remote sensing 2022年第2期16卷

作者： Chen, Xiao Zhu, Juan Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing Peoples R China

Recognizing land type using remote sensing images is vital for land resource management and monitoring. To improve the accuracy of land scene recognition from remote sensing images captured by sensors, an innovative classification method based on an improved capsule network was proposed. First, a circular local binary pattern algorithm extracted texture features from remote sensing images. The textured images were then fused with the original remote sensing images, and a 1-by-1 convolutional layer combined the fused feature graphs linearly. Finally, an improved capsule network performed remote sensing image scene classification. The experimental results show that the average accuracy rate of the proposed method improved by more than 6% compared with that of the typical convolutional neural network method, and the proposed approach outperformed other comparable methods in terms of accuracy. The proposed method not only provides a fast convergence speed but also improves the scene classification accuracy for remote sensing images, especially for images with rich texture information. This is helpful for land remote sensing image scene classification in various applications. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： remote sensing image local binary pattern capsule network image classification

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Research on the High Resolution remote sensing image Target Detection Based on Machine Learning 3rd

Research on the High Resolution Remote Sensing Image Target ...

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3rd EAI International Conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Fu, Yanli Sun, Yingying Li, Shuyao Deng, Rui Linlin, Hou Engineering University of CAPF Shaanxi Xian China

ISBN: (纸本)9783031631351

With the development of computer technology and the national aerospace industry, remote sensing satellites have been successively projected into the air, and the resolution of remote sensing images is becoming higher and clearer. The surface information contained in remote sensing images can be used not only in civil fields such as forestry, agriculture, water conservancy, but also in the military and national defense fields. Current target detection technologies have achieved good results in natural image detection, but their performance in remote sensing images is poor. This is due to the high resolution of remote sensing images themselves, the small size of key targets in the image, and the dense distribution in multiple directions, while the background information is very complex and covers the entire range of the image, resulting in low detection accuracy and slow speed issues when using traditional methods for target detection. High resolution remote sensing images have a wide range of utilization scenarios. The traditional remote sensing image target detection based on artificial vision recognition and processing has been difficult to effectively cope with and meet the practical needs of large-scale high-resolution remote sensing impact target recognition. Based on this, this paper first analyzes remote sensing image information extraction methods based on machine learning, then studies high-resolution remote sensing image target detection methods based on machine learning, and finally presents the development trend of high-resolution remote sensing image target detection based on machine learning. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： remote sensing

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Evaluation of advanced time series similarity measures for object-based cropland mapping

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INTERNATIONAL JOURNAL OF remote sensing 2023年第12期44卷 3777-3800页

作者： He, Weiqiang Ma, Lei Yan, Ziyun Lu, Heng Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing 210023 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

With more and more high-resolution remote-sensing data becoming available, satellite image time series (SITS) analysis is often used to map crops at the pixel level. Although the object-based image analysis paradigm has proven superior to pixel-based image analysis for overcoming salt-and-pepper effects, object-based time series crop classification is still rare. Except for time-weighted dynamic time warping (TWDTW), other time series similarity measuring algorithms popular in the signal processing domain, such as shape-based distance (SBD) and global alignment kernel (GAK), remain poorly explored at the object level. Accordingly, this study aimed to explore better object-based time series classification frameworks by investigating similarity measures, including SBD, GAK and TWDTW, and thus experiments were designed to analyse the response of these methods to various parameters, including crop types, time series density, processing efficiency and so on. The results show that GAK is superior to SBD and TWDTW in general. Specifically, GAK performs better than TWDTW with the limited available images, e.g. in cloudy and rainy climate regions. Also, GAK outperforms SBD in identifying crops with a complex phenological pattern, such as alfalfa. Furthermore, TWDTW has the lowest processing efficiency. This study addresses the scarcity of similarity measurement methods in the object-based time series paradigm, and it is expected that this study could provide some guidance for the selection of cropland mapping methods.

关键词： Crop classification satellite image time series object-based image analysis time series similarity measures

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Few-shot real scenes convolutional anchor clustering twin network for hyperspectral image classification

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IET image processing 2025年第1期19卷

作者： Zhou, Xiangshan Tong, Xiaoyi Zhou, Xuchuan Wang, Lin Zhang, Lei Zhou, Jie Qin, Su PowerChina Chengdu Engn Corp Ltd Chengdu Peoples R China Southwest Minzu Univ State Key Lab Comp Sci State Ethn Affairs Commiss Chengdu 610043 Peoples R China Southwest Minzu Univ Sch Comp Sci & Engn Chengdu Peoples R China

In order to improve the performance of hyperspectral open set recognition processing with insufficient sample size, the authors propose a new supervised contrastive learning (SCL) framework (FSSCL-OSC) that can achieve open-set classification of hyperspectral images in scenarios with very few sample sizes, which consists of three steps. First, supervised contrastive learning (SCL) with geometric transformations is designed, which uses rotated labels as supervision to obtain lower-level characteristics that more accurately capture various orientations and then makes use of a spectral-spatial characteristic extraction network to maximize the utilization of HSI's spectrum and spatial information. Next, a class anchor open-set classification module based on a twin clustering network is designed, which uses SCL and FSL to extract more specific personal information by mining the category-invariant characteristics present in the known versus unknown class data. Finally, multiple convolution and open-set recognition (OSC) is performed on the feature blocks. Experimental results on three classical HSI datasets show that FSSCL-OSC provides a significant improvement over existing methods, under a sample size of only 10%, the overall accuracy reached 82.38%, 90.76% and 84.70%, respectively.

关键词： image sensors remote sensing

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Generative Diffusion Models for Compressed sensing of Satellite LiDAR Data: Evaluating image Quality Metrics in Forest Landscape Reconstruction

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remote sensing 2025年第7期17卷 1215-1215页

作者： Ramirez-Jaime, Andres Arce, Gonzalo R. Porras-Diaz, Nestor Ieremeiev, Oleg Rubel, Andrii Lukin, Vladimir Kopytek, Mateusz Lech, Piotr Fastowicz, Jaroslaw Okarma, Krzysztof Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Natl Aerosp Univ Dept Informat & Commun Technol UA-61070 Kharkiv Ukraine West Pomeranian Univ Technol Szczecin Dept Signal Proc & Multimedia Engn PL-70313 Szczecin Poland

Spaceborne LiDAR systems are crucial for Earth observation but face hardware constraints, thus limiting resolution and data processing. We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing the onboard computational load and optimizing data transmission. Diffusion models then reconstruct detailed HHDCs from sparse samples on Earth. To ensure reliability despite lossy compression, we analyze distortion metrics for derived products like Digital Terrain and Canopy Height Models and evaluate the 3D reconstruction accuracy in waveform space. We identify image quality assessment metrics-ADD_GSIM, DSS, HaarPSI, PSIM, SSIM4, CVSSI, MCSD, and MDSI-that strongly correlate with subjective quality in reconstructed forest landscapes. This work advances high-resolution Earth observation by combining efficient data handling with insights into LiDAR imaging fidelity.

关键词： canopy height model (CHM) compressive sampling digital terrain model (DTM) light detection and ranging (LiDAR) machine learning (ML) image quality assessment (IQA) hyperheight data cube (HHDC)

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Three-Dimensional High-Resolution Space-Borne ISAR Imaging With Compact Antenna Configuration for Large Rotational Angle

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2024年 17卷 7422-7435页

作者： Gong, Rui Wang, Ling Zhu, Daiyin Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 211106 Peoples R China

Space situation awareness (SSA) has gained much more attention in the last decades due to the increasing number of spacecraft in space, which provides powerful space surveillance, and hence, ensures the safe flight of spacecraft. Inverse synthetic aperture radar (ISAR) is an effective tool for uncooperative target recognition and has been widely used in ground-based SSA systems. Due to the increasing usage of small spacecraft in missions and the similar appearance of most spacecraft, the high-resolution imaging is more highly needed for reliable spacecraft recognition than before, which is also desired by the component-level recognition. Furthermore, the 3-D image reconstruction of spacecraft is very helpful in evaluating the structures due to the addition of the 3-D information, and hence, has become a very promising tool for deep target analysis. This article proposes a high-resolution spaceborne 3-D ISAR imaging method with fewer requirements on the antenna configuration and high computational efficiency. First, dedicated processing is designed to get high azimuthal resolution using large target-to-radar rotational angle accumulation. We address associated issues including the migration through resolution cell phenomenon and the high-order azimuthal phase modulation leading to defocusing of the resultant spacecraft images. Second, with the generated high-resolution 2-D images, we use dual-antenna interferometric processing to acquire the 3-D coordinates. To get precise coordinates of scatterers in both vertical and horizontal directions, the cross-range scaling technique is used. Finally, interferometric processing is used for locating the targets' height coordinates. Simulation results indicate that the presented method can provide 3-D images of spacecraft of higher resolution with a simple system structure than the traditional interferometric ISAR image methods.

关键词： Imaging Radar imaging Three-dimensional displays Space vehicles image resolution Receiving antennas Antennas Interferometric inverse synthetic aperture radar (InISAR) polar format algorithm (PFA) space targets 3-D imaging

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Challenges and solutions for automated avian recognition in aerial imagery

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remote sensing IN ECOLOGY AND CONSERVATION 2023年第4期9卷 439-453页

作者： Miao, Zhongqi Yu, Stella X. Landolt, Kyle L. Koneff, Mark D. White, Timothy P. Fara, Luke J. Hlavacek, Enrika J. Pickens, Bradley A. Harrison, Travis J. Getz, Wayne M. Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA USA Univ Calif Berkeley Int Comp Sci Inst Berkeley CA 94720 USA US Geol Survey Upper Midwest Environm Sci Ctr La Crosse WI USA US Fish & Wildlife Serv Div Migratory Bird Management Orono ME 04431 USA Bur Ocean Energy Management Environm Studies Program Sterling VA USA US Fish & Wildlife Serv Div Migratory Bird Management Laurel MD USA Univ KwaZulu Natal Sch Math Stat & Comp Sci Durban South Africa

remote aerial sensing provides a non-invasive, large geographical-scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long-tailed) data distribution, (2) annotation uncertainty in categorization, and (3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan in the United States to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re-Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft-fine Pseudo-Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state-of-the-art computer science, thereby opening new doors to future research.

关键词： Avian conservation avian image recognition deep learning remote sensing

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A Robust Algorithm for Reading recognition of Pointer Instrument with Local Matching 12

A Robust Algorithm for Reading Recognition of Pointer Instru...

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

作者： Niu, Tun 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

The inspection and recognition of readings on pointer instruments are important tasks in power inspection. Due to the diversity and the complex dial information of pointer instruments, the adaptability and low accuracy of existing methods are not enough. In this paper, we proposed a pointer instrument reading recognition algorithm based on local fast matching. Firstly, YOLOv8 is used to locate and segment the instrument in the inspection image. The positions and values of key scale are marked in the template image. Based on the key local information, a mapping relationship is established between the instrument location region in the inspection image and the template image. Then, the Hough transform is used to detect the position of the pointer, map it to the template image, and finally calculate the instrument reading based on the relative positions of the key graduations on the template image and the mapped pointer position. Experimental results demonstrate that the proposed method achieves a relative error within 5% for pointer instrument reading recognition in indoor substations. © 2024 SPIE.

关键词： Inspection

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Efficient Aero-Optical Degraded image Restoration via Adaptive Frequency Selection

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remote sensing 2025年第7期17卷 1122-1122页

作者： Huang, Yingjiao Zhang, Qingpeng Ma, Xiafei Ma, Haotong Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Chinese Acad Sci Key Lab Opt Engn Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China

During high-speed flight, the aircraft causes rapid compression of the surrounding air, creating a complex turbulent flow field. This high-speed flow field interferes with the optical transmission of optical imaging systems, resulting in high-frequency random displacement, blurring, intensity attenuation, or saturation of the target scene. Aero-optical effects severely degrade imaging quality and target recognition capabilities. Based on the spectral characteristics of aero-optical degraded images and the deep learning approach, this paper proposes an adaptive frequency selection network (AFS-NET) for correction. To learn multi-scale and accurate features, we develop cascaded global and local attention mechanism modules to capture long-distance dependency and extensive contextual information. To deeply excavate the frequency component, an adaptive frequency separation and fusion strategy is proposed to guide the image restoration. Integrating both spatial and frequency domain processing and learning the residual representation between the observed data and the underlying ideal data, the proposed method assists in restoring aero-optical degraded images and significantly improves the quality and efficiency of image reconstruction.

关键词： aero-optics image restoration deep learning frequency domain learning

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Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing's Central Urban Area

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remote sensing 2025年第6期17卷 990-990页

作者： Zhang, Yongchuan Xu, Yuhong Gao, Jie Zhao, Zunya Sun, Jing Mu, Fengyun Chongqing Jiaotong Univ Smart City Dept Chongqing 402260 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518034 Peoples R China

Urban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods face significant challenges, such as difficulties in effectively integrating multi-source heterogeneous data, capturing dynamic spatiotemporal patterns, and addressing the complex interrelationships among various data types. These issues significantly limit the applicability of UFZ mapping in complex urban scenarios. To address these challenges, this paper proposes a tripartite neural network (TriNet) for multimodal data processing, including remote sensing (RS) images, Point of Interest (POI) data, and Origin-Destination (OD) data, fully utilizing the complementarity of different data types. TriNet comprises three specialized branches: ImgNet for spatial features extraction from images, POINet for functional density distribution features extraction from POI data, and TrajNet for spatiotemporal pattern features extraction from OD data. Finally, the method deeply fuses these features through a feature fusion module, which utilizes a two-layer fully connected network for deep fusion, allowing the model to fully utilize the interdependencies among the data types, significantly improving the UFZ classification accuracy. The experimental data are generated by mapping OpenStreetMap (OSM) vector into conceptual representations, integrating images with social sensing data to create a comprehensive UFZ classification benchmark. The method achieved an overall accuracy of 84.13% on the test set of Chongqing's main urban area, demonstrating high accuracy and robustness in UFZ classification tasks. The experimental results show that the TriNet model performs effectively in UFZ classification.

关键词： multi-source data remote sensing social sensing deep learning urban functional zone mapping

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