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Pol-SAR image classification using multifarious stratification stratagem in machine learning

Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 411-430页

作者： Ashwin, P.V. Ansal, K.A. Saintgits College of Engineering APJ Abdul Kalam Kerala Technological University Kerala India

image classification using polarimetric synthetic aperture radar (Pol-SAR) is becoming more important in image processing for remote sensing applications. However, in the existing techniques, during the feature extraction process, there exist some limitations including laborious endeavour for Pol-SAR image classification, identifying intrinsic features for target recognition is difficult in feature selection, and pixel-level Pol-SAR image classification is difficult for obtaining more precise and coherent interpretation consequences. Hence to overcome these issues, a novel Multifarious Stratification Stratagem in machine learning is proposed to achieve pixel-level Pol-SAR classification. In this proposed model, a novel Scrumptious Integrant Wrenching method is used for efficient feature extraction. It is compatible with the orientation-sensitive of the Pol-SAR image which increases the variety of intra-layer features. To remove the difficulty in feature selection, a novel Episodicical Proximity Selection method is proposed in which a Split-level parallel feature selection strategy is used to select the best qualities from the extracted features. To tackle the difficulty in classification, an Elastic Net Classifier (ENC) is used that find the coefficient vector for the linear combination of the training sets. This efficiently classified the best features in the Pol-SAR images and improved the proposed system’s accuracy. As a result, the performance measures of the proposed system demonstrate that the accuracy is increased by 99.69%, precision is increased by 98.99%, recall is increased by 98.99%, sensitivity is increased by 98.99%, and F1-score is increased by 98.99% as a response. © 2025 – IOS Press. All rights reserved.

关键词： Feature Selection

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GSGAN: Learning controllable geospatial images generation

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IET image processing 2023年第2期17卷 401-417页

作者： Su, Xingzhe Lin, Yijun Zheng, Quan Wu, Fengge Zheng, Changwen Zhao, Junsuo Chinese Acad Sci Inst Software 4 Nansi St Zhongguancun Beijing Peoples R China Univ Chinese Acad Sci 19 Yuquan Rd Beijing Peoples R China Chinese Acad Sci Inst Software 4 Nansi St Zhongguancun Beijing 100190 Peoples R China

Compared with natural images, geospatial images cover larger area and have more complex image contents. There are few algorithms for generating controllable geospatial images, and their results are of low quality. In response to this problem, this paper proposes Geospatial Style Generative Adversarial Network to generate controllable and high-quality geospatial images. Current conditional generators suffer the mode collapse problem in geospatial field. The problem is addressed via a modified mode seeking regularization term with contrastive learning theory. Besides, the discriminator network architecture is modified to process global feature information and texture information of geospatial images. Feature loss in the generator is introduced to stabilize the training process and improve generated image quality. Comprehensive experiments are conducted on UC Merced Land Use Dataset, NWPU-RESISC45 Dataset, and AID Dataset to evaluate all compared methods. Experiment results show our method outperforms state-of-the-art models. Our method not only generates high-quality and controllable geospatial images, but also enhances the discriminator to learn better representations.

关键词： feature extraction Geospatial Style Generative Adversarial Network image classification learning (artificial intelligence) Geophysics computing image texture high-quality geospatial images Neural nets Optical, image and video signal processing complex image contents Computer vision and image processing techniques natural images global feature information geospatial field remote sensing current conditional generators image recognition generated image quality mode collapse problem Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research texture information geophysical image processing controllable geospatial images generation

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Multi-task deep learning model for quantitative volatile organic compounds analysis by feature fusion of electronic nose sensing

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SENSORS AND ACTUATORS B-CHEMICAL 2024年 417卷

作者： Ni, Wangze Wang, Tao Wu, Yu Liu, Xue Li, Zhuoheng Yang, Rui Zhang, Kai Yang, Jianhua Zeng, Min Hu, Nantao Li, Bin Yang, Zhi Shanghai Jiao Tong Univ Natl Key Lab Adv Micro & Nano Manufacture Technol Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Micro Nano Elect Shanghai 200240 Peoples R China East China Univ Sci & Technol Sch Mech & Power Engn Shanghai Key Lab Intelligent Sensing & Detect Tech Shanghai 200237 Peoples R China Shanghai Marine Diesel Engine Res Inst Shanghai 201108 Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Zhengzhou 450002 Peoples R China

In exploring pattern recognition for electronic noses via deep neural networks, traditional networks encounter key challenges, such as low training efficiency, and neglect of spatial-temporal attributes of gas sensor response sequences. In this study, an unmanned gas-sensing test system is used to generate a large dataset to ensure robust model training. The Gramian angular field-Markov transition field is utilized to convert time sequences into images. Using advanced image processing tools, the images are then subsequently compressed with data augmentation. This method fully preserves temporal and spatial features within the sequences, thus enhancing model performance. Furthermore, the proposed multi-task learning (MTL) framework competently performs simultaneous classification and regression tasks. The primary component of the MTL network, majorly constituted of convolutional neural networks, emphasizes the spatial features of the sequences. The integration of a long short-term memory layer ensures the preservation of temporal feature analysis of the input data, thereby enhancing predictive performance. When images are compressed to only 3.9 % of the original data, substantial information can still be preserved. Subsequently, the model trained by such compressed images attains an accuracy of 95.31 % and an R2 score of 0.9510 for classification and regression tasks, respectively. Our work reveals the remarkable potential of integrating temporal and spatial features in pattern recognition, promoting the potency of multi-tasking deep learning networks in electronic nose technology.

关键词： Electronic nose image conversion Convolutional neural network Long short-term memory network Feature fusion

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A multiscale fuzzy dual-domain attention network for urban remote sensing image segmentation

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INTERNATIONAL JOURNAL OF remote sensing 2022年第14期43卷 5480-5501页

作者： Chong, Qianpeng Xu, Jindong Jia, Fei Liu, Zhaowei Yan, Weiqing Wang, Xuan Song, Yongchao Yantai Univ Sch Comp & Control Engn Yantai Peoples R China

Semantic segmentation of high-resolution remote sensing images plays an important role in the remote sensing community. However, many indistinguishable objects are prevalent within urban remote sensing images, and some objects belonging to the same class are different and many objects that do not belong to the same class are similar. These tricky objects make the images exhibit low-interclass variance and high-intraclass variance, which significantly limits segmentation performance. Therefore, a fresh insight was presented to alleviate this issue by incorporating the fuzzy pattern recognition method and deep-learning method. Specifically, we proposed a multiscale fuzzy dual-domain attention network (MFDAN). In MFDAN, a two-dimensional Gaussian fuzzy learning module is proposed to eliminate those factors that influence the intraclass and interclass variance. In addition, a dual-domain attention module is proposed to derive more informative semantic representations in the channel and spatial domains, respectively. These two modules will be integrated in a multiscale perspective. Extensive experiments on the benchmark datasets illustrate qualitatively and quantitatively that the proposed MFDAN is competitive.

关键词： Attention mechanism fuzzy learning remote-sensing imagery semantic segmentation

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Inducing Causal Meta-Knowledge From Virtual Domain: Causal Meta-Generalization for Hyperspectral Domain Generalization

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2024年 62卷

作者： Wang, Haoyu Liu, Xiaomin Qiao, Zhenzhuang Tao, Hanqing China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Space Minist Educ Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

Cross-domain hyperspectral image (HSI) classification can improve the model's classification performance in the target domain by utilizing the rich knowledge from the source domain. However, existing cross-domain HSI classification methods mostly belong to transductive learning, which is difficult to apply to domain generalization tasks where the target domain is unseen during model learning. Inspired by human causal reasoning and knowledge induction mechanisms, this article develops an inductive learning-based framework for hyperspectral domain generalization: Causal meta-generalization. By simulating domain generalization scenarios, the framework helps the model induct domain-invariant causal meta-knowledge, thereby ensuring its strong generalization ability to unseen target domains. Specifically, we first propose a bottleneck variational auto-encoder (B-VAE) based on a forward-reverse information bottleneck, decoupling the domain distribution and class distribution of HSIs. By perturbing the domain distribution to generate virtual domains, we simulate potential domain distribution changes in the real world, providing a data basis for the induction of causal meta-knowledge. Second, in the process of simulating domain generalization scenarios, we establish a dual-layer optimization mechanism (DLOM) based on invariant-generalization risk minimization. In the inner layer optimization, by minimizing the model's invariant causal effect loss (ICEL) in the virtual source domains, we guide the model to learn domain-invariant causal meta-knowledge. In the outer optimization, by minimizing the model's generalization risk in the unseen virtual target domain, we enhance the applicability of causal meta-knowledge in domain generalization tasks. This proposed method has potential applications in remote sensing signal-processing tasks, such as the recognition of crop pests and diseases and the identification of minerals. The code can be accessed at https://***/wzr78998/CM

关键词： Data models Optimization Adaptation models Training Risk minimization Knowledge transfer Hyperspectral imaging Data mining Biological system modeling Annotations Classification cross-domain domain generalization hyperspectral image (HSI) meta-knowledge

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A KIND OF BROADBAND TARGET JAMMER OF SYNTHEIC APERTURE RADAR BASED ON INFORMATION METASURFACE

A KIND OF BROADBAND TARGET JAMMER OF SYNTHEIC APERTURE RADAR...

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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Li, Hua Li, Zhenning Liu, Kaiyu Xu, Kaijiang Luo, Chao Lv, You Deng, Yunkai Chinese Acad Sci Aerosp Informat Res Inst Dept Space Microwave Remote Sensing Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100039 Peoples R China

ISBN: (纸本)9798350360332;9798350360325

Over the past decade, jamming methods for Synthetic Aperture Radar (SAR) target detection and recognition, such as generating false targets, electromagnetic (EM) deception, and spoofing, have garnered significant attention. Distinguished from conventional SAR jamming methods, the target jamming scenario for SAR based on a new man-made material cloak introduces enhanced possibilities. These technologies exhibit the capability to effectively scatter incident EM waves in arbitrary or desired directions, thereby concealing pertinent information associated with the critical target. In this paper, we propose a broadband target jammer of SAR based on an information metasurface, which integrates intelligent information processing algorithms with space-time-coding digital metasurface, providing the capability to manipulate incident EM waves freely to achieve multi-mode jamming protections for critical targets. In simulation experiments, the results demonstrate adjustable EM deception and the generation of multiple false targets without any cooperation with the SAR system. Our work brings the available protection strategies for SAR closer to a wide range of real-time, broadband, and controllable applications, enhancing the concealment of critical targets in hot conflict zones.

关键词： Jamming protection image modulation information metasurface synthetic aperture radar

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Application Research of Attention Mechanism in remote sensing image Classification 5

Application Research of Attention Mechanism in Remote Sensin...

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5th International Conference on Applied Machine Learning, ICAML 2023

作者： Feng, Hui Li, Yabin Yu, Qiaochan Zhang, Mingrui China Waterborne Transport Research Institute Beijing China Qingdao Shipping Development Research Institute Qingdao China

ISBN: (纸本)9798350341416

The image classification as the key technology in the remote sensing system is mainly based on the image with the characteristics of the electromagnetic wave radiation information of the ground object to distinguish and identify the category of the ground object. The computer classification of remote sensing images is to achieve the purpose of automatic classification and recognition of ground objects through numerical processing of pixels. A high-precision remote sensing image classification method is a prerequisite for various applications. Compared with manual calibration of images, which is time-consuming and laborious, it is necessary to use machine learning methods to automatically process images. © 2023 IEEE.

关键词： image classification

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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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RFL-CDNet: Towards accurate change detection via richer feature learning

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pattern recognition 2024年 153卷

作者： Gan, Yuhang Xuan, Wenjie Chen, Hang Liu, Juhua Du, Bo Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Wuhan Univ Inst Artificial Intelligence Wuhan Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan Peoples R China MNR Land Satellite Remote Sensing Applicat Ctr Beijing Peoples R China

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learningbased change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub -optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state -of -the -art performance on WHU cultivated land dataset and CDD dataset, and the second best performance on WHU building dataset. The source code and models are publicly available at https://***/Hhaizee/RFL-CDNet.

关键词： Change detection Richer feature learning Coarse-to-fine Learnable fusion

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Forest canopy information extraction in Gannan Plateau based on UAV high-resolution images

Forest canopy information extraction in Gannan Plateau based...

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2023 International Conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Wu, Yanduo Han, Hui Faculty of Geomatics Lanzhou Jiaotong University Lanzhou730070 China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou730070 China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou730070 China

ISBN: (纸本)9781510666351

In order to explore the feasibility of applying UAV remote sensing and object-oriented canopy extraction technology to forest areas with different canopy densities, 26 forest plots located in Gannan Plateau were selected and divided into two categories of coniferous forests with medium and high canopy densities. ESP tool was used to screen the optimal segmentation scale for multi-scale segmentation, and the feature space was optimized for object-oriented classification. The east-west and north-south crown amplitudes on quadrat scale were extracted in ArcGIS. The extracted crown amplitude was compared with the field measured data to evaluate the accuracy. The results were as follows: In UAV image extraction, the fitting values R2 and RMSE of east and west canopy width and measured values were 0.865 and 1.025m, respectively, and the fitting values R2 and RMSE of north and south canopy width were 0.867 and 1.235m, respectively. The R2 and RMSE of east and west canopy width with high canopy closure were 0.828 and 1.145m, respectively, and the R2 and RMSE of north and south canopy width were 0.787 and 1.523m, respectively. The extraction effect of the plots with medium canopy closure was better than that of the plots with high canopy closure, and both of the two canopy closure met the accuracy requirements. The results show that the automatic extraction method of canopy parameter information based on UAV remote sensing image is efficient and reliable, and has important practical significance in forest resources investigation. © 2023 SPIE.

关键词： remote sensing

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