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Data processing for object recognition from satellite images using convolutional neural networks: a case study in solar panel recognition with FORMOSAT-2

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JOURNAL OF APPLIED remote sensing 2024年第3期18卷

作者： Chen, Bo-Wei Hsu, Hwai-Jung Chang, Yu-Yun Liu, Cynthia S. J. Huang, Winfred Feng Chia Univ Dept Informat Engn & Comp Sci Taichung Taiwan Feng Chia Univ Res Ctr Artificial Intelligence Taichung Taiwan Taiwan Space Agcy Hsinchu Taiwan

Investigating ground objects widely distributed in geography and large in scale is one of the primary missions for satellite sensors. On the other hand, recognizing objects from images is one of the classic tasks for convolutional neural networks (CNNs), currently the most popular computer vision technique. Data processing, such as data augmentation, channel selection, and image fusion, can be essential when applying CNNs to satellite images. With a case study of recognizing solar panels from satellite images using CNN, the related data processing issues are discussed, and an approach to embed channel fusion methods into CNN is established. As a result, the following findings are concluded from our case study: (1) not all channels in satellite images contribute to specific object recognition, and thus channel selection is necessary in applying CNN on satellite images;(2) fine-tuning the fusion method embedded in CNN improves the model stability;and (3) transfer learning is outperformed by CNN models trained with augmented data for object recognition from satellite images.

关键词： convolutional neural network object recognition satellite images transfer learning

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Deep Multimodal Fusion Model for Building Structural Type recognition Using Multisource remote sensing images and Building-Related Knowledge

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2023年 16卷 10073-10087页

作者： Zhou, Yuhang Tan, Yihua Wen, Qi Wang, Wei Li, Lingling Li, Zhenxing Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl key Lab Multispectral Informat Intelligent Pr Wuhan 430074 Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 100094 Peoples R China Natl Disaster Reduct Ctr China Minist Emergency Management Beijing 100124 Peoples R China

Building structural type (BST) information is vital for seismic risk and vulnerability modeling. However, obtaining this kind of information is not a trivial task. The conventional method involves a labor-intensive and inefficient manual inspection process for each building. Nowadays, a few methods have explored to use remote sensing images and some building-related knowledge (BRK) to realize automated BST recognition. However, these methods have many limitations, such as insufficient mining of multimodal information and difficulty obtaining BRK, which hinders their promotion and practical use. To alleviate the shortcomings above, we propose a deep multimodal fusion model, which combines satellite optical remote sensing image, aerial synthetic aperture radar image, and BRK (roof type, color, and group pattern) obtained by domain experts to achieve accurate automatic reasoning of BSTs. Specifically, first, we use a pseudo-siamese network to extract the image feature. Second, a knowledge graph (KG) based on the BRK is constructed, and then, we use a graph attention network to extract the semantic feature from the KG. Third, we propose a novel multistage gated fusion mechanism to fuse the image and semantic feature. Our method's best overall accuracy and kappa coefficient on the dataset collected in the study area are 90.35% and 0.83, which outperforms a series of existing methods. Through our model, high-precision BST information can be obtained for earthquake disaster prevention, reduction, and emergency decision making.

关键词： Building structural types (BSTs) knowledge graph (KG) multimodal fusion remote sensing

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Application of remote sensing in retrieving combustible fuel load in Guizhou region

Application of remote sensing in retrieving combustible fuel...

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

作者： Zhang, Sihang Yang, Zhi Zhu, Kuanjun Liu, Bin Zhou, Lixian Han, Junke Liu, Chang China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781510680425

Guizhou Province, situated in the southwest of China, boasts diverse and complex geographical environments and abundant forest resources. However, it faces threats from natural disasters like forest fires. Accurate estimation of combustible fuel load in Guizhou is crucial for assessing fire risks and implementing effective fire management strategies. This paper employs remote sensing technology, utilizing various remote sensing datasets including satellite imagery and ground observation data, combined with geostatistical methods to retrieve combustible fuel load in Guizhou. Furthermore, the application value of the retrieval results in fire prevention management and forest resource protection is discussed. © 2024 SPIE.

关键词： Risk management

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Self-supervised multimodal change detection based on difference contrast learning for remote sensing imagery

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pattern recognition 2025年 159卷

作者： Hou, Xuan Bai, Yunpeng Xie, Yefan Zhang, Yunfeng Fu, Lei Li, Ying Shang, Changjing Shen, Qiang Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated Aerosp Ground Ocean Big D Shaanxi Prov Key Lab Speech & Image Informat Proc Xian 710129 Peoples R China Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Wales Shaanxi Satellite Applicat Ctr Nat Resources Xian 710065 Peoples R China Northwestern Polytech Univ Sch Software Xian 710129 Peoples R China

Most existing change detection (CD) methods target homogeneous images. However, in real-world scenarios like disaster management, where CD is urgent and pre-changed and post-changed images are typical of different modalities, significant challenges arise for multimodal change detection (MCD). One challenge is that bitemporal image pairs, sourced from distinct sensors, may cause an image domain gap. Another issue surfaces when multimodal bi-temporal image pairs require collaborative input from domain experts who are specialised among different image fields for pixel-level annotation, resulting in scarce annotated samples. To address these challenges, this paper proposes a novel self-supervised difference contrast learning framework (Self-DCF). This framework facilitates networks training without labelled samples by automatically exploiting the feature information inherent in bi-temporal imagery to supervise each other mutually. Additionally, a Unified Mapping Unit reduces the domain gap between different modal images. The efficiency and robustness of Self-DCF are validated on five popular datasets, outperforming state-of-the-art algorithms.

关键词： Self-supervised learning Change detection Multimodal image remote sensing

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Privacy-Preserving remote sensing image Generation and Classification With Differentially Private GANs

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IEEE SENSORS JOURNAL 2023年第18期23卷 20805-20816页

作者： Huang, Yujian Cao, Lei Chengdu Univ Technol Coll Comp Sci & Cyber Secur Chengdu 610059 Peoples R China China West Normal Univ Coll Educ Nanchong 637002 Peoples R China

Generative adversarial networks (GANs) have demonstrated their remarkable capacity to learn the training data distribution and produce high-quality synthetic images, which have been widely adopted in image recognition tasks in remote sensing (RS) research communities. However, previous work has shown that using GANs does not preserve privacy, e.g., being susceptible to membership attacks, while sensitive information is vulnerable to nefarious activities. This drawback is considered severe in RS communities, in which critical researches highly value the security and privacy of the image content. Thus, to publicly share sensitive data for supporting critical researches and, in the meantime, guarantee the model accuracy trained from privacy-preserving data, this work develops GANs within the differential privacy (DP) framework and proposes an RS differentially private generative adversarial network (RS-DPGAN) for both privacy-preserving synthetic image generation and classification. Our RS-DPGAN is capable of releasing safe version of synthetic data while obtaining favorable classification results, which gives rigorous guarantees for the privacy of sensitive data and balance between the model accuracy and privacy-preserving degree. Both extensive empirical and statistical results confirm the effectiveness of our framework.

关键词： Differential privacy (DP) generative adversarial networks (GANs) hyperspectral image (HSI) classification privacy-preserving machine learning (ML)

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Extracting Agricultural Parcel Boundaries From High Spatial Resolution remote sensing images Based on a Multi-Task Deep Learning Network With Boundary Enhancement Mechanism

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IEEE ACCESS 2024年 12卷 112038-112052页

作者： Ma, Hai-Rong Shen, Xiang-Cheng Luo, Zhi-Qing Chen, Ping-Ting Guan, Bo Zheng, Ming-Xue Yu, Wen-Sen Hubei Acad Agr Sci Inst Agr Econ & Technol Wuhan 430064 Peoples R China Wuyi Univ Fujian Key Lab Big Data Applicat & Intellectualiza Nanping 353000 Peoples R China

Extracting agricultural parcel boundaries from remote sensing images based on deep learning methods is currently the most promising method. Due to the diversity of agricultural types and limitations of deep learning networks, parcel boundaries extracted by edge detection deep learning networks lack target specificity, and parcel boundaries extracted by semantic segmentation deep learning networks generally causes a loss of precise boundary information. Based on the principles of multi-task deep learning networks Psi-Net, BsiNet, and ResUNet-a, we constructed a parallel multi-task deep learning network M_ResUnet. The M_ResUnet is capable of performing edge detection and semantic segmentation simultaneously, and it is designed for extracting agricultural parcel boundaries from high-resolution remote sensing images. To improve the effectiveness of parcel boundary recognition, the edge enhancement concept from the context-aware tracing strategy (CATS) is introduced into the M_ResUnet, which enhanced the continuity and effectiveness of parcel boundary recognition and accelerated network training convergence. Finally, we conducted experiments on three different study areas with different remote sensing data sources and agricultural parcel types. The experimental results demonstrate that the method we proposed achieved better continuity in identifying parcel boundaries, and it also improved the recognition of boundaries between neighboring parcels with strong similarities remote sensing features such as texture and spectral characteristics.

关键词： Deep learning Feature extraction image edge detection remote sensing Semantic segmentation Data mining Multitasking Agriculture agricultural parcels deep learning semantic segmentation edge detection multi-tasking boundary tracking enhancement

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An Intelligent Two-Stage Hybrid Hierarchical Classification using Optimized Label Propagation for remote sensing image Retrieval

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JOURNAL OF THE INDIAN SOCIETY OF remote sensing 2025年 1-17页

作者： Sudha, S. K. Aji, S. Univ Kerala Ctr Data Sci & Informat Proc Dept Comp Sci Thiruvananthapuram 695581 Kerala India

The increasing volume and heterogeneous nature of remote sensing data necessitate efficient and accurate image retrieval systems, particularly for land use and land cover (LULC) mapping. This paper presents an advanced framework that integrates an optimized label propagation network (OLPNet) with a two-stage hybrid hierarchical classification (TS-H2C) approach for efficient remote sensing image retrieval (RSIR). The TS-H2C-OLPNet framework employs two sparse kernel learning machines- the relevance vector machine (RVM) and support vector machine (SVM), to enhance label distribution in complex, high-dimensional feature spaces. The framework utilizes a reconstruction-based relational autoencoder (RAE) to extract robust deep features with reduced dimensionality. In the Stage-1 hierarchy, the RVM generates confidence scores to determine super-class labels, from there the OLPNet propagates respective sub-class labels to the Stage-2 SVM. This approach efficiently manages the search space, speeds up retrieval, and effectively handles highly overlapping LULC classes, improving the recognition of unseen categories. Extensive experiments on benchmark RSI datasets with challenging scene categories demonstrate that the TS-H2C-OLPNet framework achieve SOTA Precision, Recall, and F1-score performance. Results ensure that integrating optimized label propagation and hybrid hierarchical classification can offer a robust solution for large-scale retrieval using complex LULC data.

关键词： Hybrid hierarchical classification Relational autoencoder Relevance vector machine remote sensing image retrieval Optimized label propagation Support vector machine

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An optimized run-length based algorithm for sparse remote sensing image labeling

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Defence Technology（防务技术） 2022年第4期18卷 663-677页

作者： Shen-shen Luan Bo-wen Cheng Shuai Jiang Yu-hang Wu Zong-ling Li Ji-yang Yu Institute of Spacecraft System Engineering Haidian DistrictBeijingChina

Labeling of the connected components is the key operation of the target recognition and segmentation in remote sensing *** conventional connected-component labeling(CCL) algorithms for ordinary optical images are considered time-consuming in processing the remote sensing images because of the larger size.A dynamic run-length based CCL algorithm(Dy RLC) is proposed in this paper for the large size,big granularity sparse remote sensing image,such as space debris images and ship *** addition,the equivalence matrix method is proposed to help design the pre-processing method to accelerate the equivalence labels *** result shows our algorithm outperforms 22.86% on execution time than the other algorithms in space debris image *** proposed algorithm also can be implemented on the field programming logical array(FPGA) to enable the realization of the real-time processing on-board.

关键词： Connected-component labeling Label equivalence resolving Hardware implementation Equivalence matrix On-board processing

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Landslide extraction model for remote sensing images based on improved DeepLabv3+

Landslide extraction model for remote sensing images based o...

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

作者： Wu, Yanling Lang, Wenhui School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781510680425

In order to realize the accurate recognition of landslides in remote sensing images, an improved DeepLabv3+ landslide extraction model is proposed in this paper.(1) Hybrid Module and Attention Module based CSPNet (HA-CSPNet) is constructed as the backbone feature extraction network to enhance the feature information of small landslides and suppress the interference of irrelevant background information.(2) Combining the advantages of Residual Structure and Dense Connected Module, Residual-dense ASPP is designed to focus landslide features at different scales, enhance feature reuse and prevent gradient *** experimental results show that the landslide extraction model proposed in this paper is *** can improve the accuracy of landslide semantic segmentation. © 2024 SPIE.

关键词： Semantic Segmentation

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A novel CNN-Transformer network for cloud detection in remote sensing image

A novel CNN-Transformer network for cloud detection in remot...

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

作者： Ai, Xinkai Sun, Lin College of Geodesy and Geomatics Shandong University of Science and Technology Shandong Province Qingdao266590 China

ISBN: (纸本)9781510680425

Convolutional neural networks (CNNs) are the mainstream model for extracting rich features in deep learning-driven studies on cloud detection for remote sensing images. However, due to the limitation of receptive fields in convolutional operations, a single CNN has limitations in mining global information and long-range dependencies, which affects the precision of cloud identification in intricate environments. To address the insufficient global feature and long-range dependency modeling of CNNs, a new CNN-Transformer network for cloud detection (CNN-TFCD) in remote sensing images is proposed in this paper. This method comprehensively utilizes strengths of both CNN and transformer to fully mine local spatial details, global contextual information, and long-range dependencies, to obtain comprehensive feature representations. The feature cascade mechanism is applied for multi-scale feature fusion, further enhancing the model's feature representation capability. Evaluations conducted on the Landsat-8 dataset demonstrate that the CNN-TFCD method achieves accurate and stable cloud detection. © 2024 SPIE.

关键词： Convolutional neural networks

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