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2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Zhu, Jingxing Liu, Rui Wang, Feng You, Hongjian Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510680012

With the rapid advancement of synthetic aperture radar (SAR) sensors, it has become more important to extract change information between high-resolution SAR images. Considering the efficacy and robustness of segmentation-based strategy in solving fine-resolution images, as well as the excellent performance of deep learning in feature extraction, therefore, in this paper, we propose an unsupervised region-level graph convolutional network (URGCN) for SAR change detection. Firstly, we propose a multi-temporal joint segmentation method to generate multi-level regions. This method can simultaneously segment bi-temporal SAR images and automatically align bi-temporal regions during segmentation process. We then perform pre-classification on difference images generated by neighborhood ratio detector. The high confidence training samples are selected through hierarchical clustering. Thirdly, we encode the segmented regions are as a graph structure and construct GCN model. The graph can capture the spatial and temporal structure information. Finally, the GCN model parameters are optimized by labeled samples and inferring unlabeled regions. Experimental results on two VHR SAR change detection datasets demonstrated that the proposed method can extract complete change information with high accuracy compared to alternative approaches. © 2024 SPIE.

关键词： Change detection

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Region Normalization for Image Inpainting

arXiv

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arXiv 2019年

作者： Yu, Tao Guo, Zongyu Jin, Xin Wu, Shilin Chen, Zhibo Li, Weiping Zhang, Zhizheng Liu, Sen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem;(2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements. Our code is available at https://***/geekyutao/RN. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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The Potential of Multi-Agent Consensus Equilibrium for Synthetic Aperture Radar processing 15

The Potential of Multi-Agent Consensus Equilibrium for Synth...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Fan, Yizhe Li, Jie Wang, Kun Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9783800762873

Multi-agent consensus equilibrium(MACE) mechanism is a generalization of popular used PnP-ADMM method in computational imaging. We propose a novel SAR processing framework based on MACE mechanism, named by MACE-SAR. The proposed framework offers the possibility to unify various SAR processing agents of both data-driven learned priors and physics-based regularization priors, enabling the integration of diverse functions and enhance the image reconstruction quality. We exemplifies how the MACE-SAR framework can be customized to combine SAR image formation, feature enhancement, image denoising, target detection, etc. The effectiveness of the framework is demonstrated through experiments conducted on simulated and Gaofen-3 satellite real data. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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SEMI-SUPERVISED OBJECT DETECTION IN REMOTE SENSING IMAGES USING GENERATIVE ADVERSARIAL NETWORKS

SEMI-SUPERVISED OBJECT DETECTION IN REMOTE SENSING IMAGES US...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Guowei Chen Lei Liu Wenlong Hu Zongxu Pan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training is often time-consuming. To address this problem, a semi-supervised learning based method is proposed in this paper. Semi-supervised learning trains detection networks with few annotated data and massive amount of unannotated data. In the proposed method, Generative Adversarial Network is applied to extract data distribution from unannotated data. The extracted information is then applied to improve the performance of detection network. Experiment shows that the method in this paper greatly improves the detection performance compared with supervised learning using only few annotated data. The results prove that it is possible to achieve acceptable detection result when only few target object is annotated in the training dataset.

关键词： Gallium nitride Generative adversarial networks Detectors Generators Airplanes Training Semisupervised learning

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An Invariant Generalized Hough Transform Based Method of Inshore Ships Detection

An Invariant Generalized Hough Transform Based Method of Ins...

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International Symposium on Image and Data Fusion (ISIDF)

作者： Jian Xu Kun Fu Xian Sun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Automatic inshore ship detection from remote sensing imagery has many important applications, such as ship change detection and harbor dynamic surveillance. Stable performance of inshore ship detection is vital to the analysis of ship change and then determines the harbor surveillance effect. However, it is hard to detect inshore ships utilizing the traditional area-based method because the grayscale and texture character of inshore ships are similar to that of the shore. In this paper, a new method based on invariant generalized Hough transform is introduced to extract ship shape using the evidence-gathering procedure. In contrast with other shape extraction methods used in inshore ships detection, our method is specially tolerant to noise and occlusion, and also invariant to translation, scale and rotation transformation. Moreover, our method can be used to separate ships moored together that can benefit to ship recognition. Experiment results are demonstrated on the optical remote sensing imagery from Google Earth.

关键词： Marine vehicles Image edge detection Transforms Noise Remote sensing Indexes Shape

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MEDIUM-EARTH-ORBIT SAR IMAGING BASED ON keySTONE TRANSFORM AND AZIMUTH PERTURBATION

MEDIUM-EARTH-ORBIT SAR IMAGING BASED ON KEYSTONE TRANSFORM A...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Lijia HUANG Bing HAN Donghui HU Chibiao DING Lihua ZHONG Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences

ISBN: (纸本)9781467311601

Due to the significant azimuth variance property in medium-Earth-orbit (MEO) synthetic aperture radar (SAR) echo, it is difficult for the conventional SAR algorithms to achieve a good compromise between accuracy and efficiency. A novel algorithm based on keystone transform (KT) and azimuth perturbation (AP) is introduced in this paper to handle this problem. The function of KT is to correct the range walk and thus to mitigate the azimuth variance effect on range processing. The function of AP is to equalize the Doppler histories in each range gate and thus to mitigate the azimuth variance effect on azimuth compressing. Simulation results of an L-band MEO SAR with 5 m resolution at 10,000 km altitude demonstrate the capability of our algorithm.

关键词： Azimuth perturbation (AP) keystone transform (KT) Medium-Earth-orbit (MEO) SAR SAR data processing Synthetic aperture radar (SAR)

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Ionosphere Estimation and Correction for Geosynchronous SAR based on point target deviation between Range Sub-images

Ionosphere Estimation and Correction for Geosynchronous SAR ...

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2016 IEEE 13th International Conference on Signal processing（ICSP2016）

作者： Huang Lijia Ding Chibiao Qiu Xiaolan Zhang Hongyi Li Liang Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences

Ionosphere is an important factor in highresolution spaceborne synthetic aperture radar（SAR） and geosynchronous（GEO） SAR. An approach based on point target deviation between range sub-images is proposed in this paper to estimate and correct the ionosphere. Due to the dispersion effect, the ionosphere causes propagation delay deviation in range subimages with different carrier frequency. This deviation can be used to estimate the total electron content（TEC） along the propagation path, and then the ionospheric effects can be corrected according to the signal model deduced out in this paper. The simulation results show that our approach is valid and robust.

关键词： Spaceborne Synthetic Aperture Radar（SAR） Geosynchronous（GEO） SAR Ionospheric Effects Estimation and Correction

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Extent and spatial distribution of terrace abandonment in China

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Journal of Geographical Sciences 2023年第7期33卷 1361-1376页

作者： DONG Shijie XIN Liangjie LI Shengfa XIE Hualin ZHAO Yuluan WANG Xue LI Xiubin SONG Hengfei LU Yahan Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System/Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China Institute of Ecological Civilization Jiangxi University of Finance and EconomicsNanchang 330013China College of Geography and Environmental Science Guizhou Normal UniversityGuiyang 550001China

Driven by urbanization and industrialization,arable land in hilly and mountainous regions of China is gradually becoming marginalized,with the extent of arable land abandonment rapidly expanding from poor-quality sloping arable land to high-quality *** abandonment of large-scale terraces will lead to a series of socio-economic and ecological effects.A national sample survey was used to investigate the extent and spatial distribution of terrace abandonment in China,and a total of 560 valid village questionnaires from 329 counties were collected in the mountainous areas of *** main findings are as follows:(1)The phenomenon of terrace abandonment was widespread throughout the country,with 54% of the total surveyed villages exhibiting terrace abandonment,and the area of abandoned terraces accounting for 9.79% of the total.(2) The degree of terrace abandonment is high in the south and low in the *** most serious region with abandonment was the hilly and mountainous areas in the south,especially in the middle and lower Yangtze River region.(3)The main driving factors of terrace abandonment were rural labor migration,agricultural mechanization level,irrigation conditions,and transportation conditions for *** measures should be taken based on the specific conditions of each area to alleviate terrace *** such as improving terrace mechanization are universally ***,low-quality terraces can be withdrawn orderly,and for high-quality terraces,multiple measures are needed to consolidate agricultural production,such as adjusting the planting structure,strengthening agricultural infrastructure construction,and encouraging the transfer of land-use rights as well as large-scale operation.

关键词： terraces arable land abandonment national sample survey spatial variation China

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DRBOX FAMILY: A GROUP OF OBJECT DETECTION TECHNIQUES FOR REMOTE SENSING IMAGES

DRBOX FAMILY: A GROUP OF OBJECT DETECTION TECHNIQUES FOR REM...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Lei Liu Zongxu Pan Guowei Chen Yizhao Gao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Objects in remote sensing images are difficult to detect due to arbitrarily rotated angles and the wide variance of scales. As bounding box plays an important role in object detection, we proposes a new bounding box type named rotated bounding box (rBox). With the application of rBox, we have proposed a series of detection techniques (DrBox, DrBoxLight, DrBoxSemi, DrBoxPro) to effectively handle the situation where the orientation angles of the objects are arbitrary. This article is a brief overview of these techniques. The original DrBox detector applies VGG-net as its main network framework, with image pyramid input to address multi-scale problem. DrBoxLight is a mini version of DrBox, which applies MobileNet and knowledge distillation to be deployed on embedded devices. DrBoxSemi is a semi-supervised version of DrBox, so annotation of all training samples is no longer necessary. DrBoxPro is the most important update for DrBox with professional designing of abundant prior-rBoxes on feature pyramid networks. In our experiments, we demonstrated how rBox helps to improve the performance of object detection compared with traditional bounding boxes. Besides, we evaluated the performance of our DrBox family on a series of object detection tasks.

关键词： Object detection Remote sensing images Rotated object Deep learning

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An adaptive Enhanced Imaging Method for Multi-aspect SAR

An adaptive Enhanced Imaging Method for Multi-aspect SAR

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IEEE International Conference on Radar

作者： Fei Teng Yun Lin Xiaoyang Yue Shanshan Feng Wen Hong Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Aerospace Information Research Institute Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665468893

Compared with traditional SAR working modes, multi-aspect SAR can provide images with higher resolution and signal-to-noise ratio (SNR) due to its larger synthetic aperture. However, the SNR does not increase with the increase of the aperture length. This is because the scattering is no longer isotropic as traditional SAR when the viewing angle is large. In this paper, an adaptive enhanced imaging method for multi-aspect SAR is proposed. The resolution and the SNR are maximized by scattering analysis performed simultaneously with imaging. In the process of image generation, the scattering characteristic is analyzed and all targets are divided into two categories: isotropic and anisotropic. Then different image formation strategies are used for isotropic and anisotropic target. A C-band circular SAR data is used to validate our method.

关键词： Image resolution C-band Scattering Imaging Radar scattering Radar imaging Apertures

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