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Monitoring Changes to Small-Sized Lakes Using Medium spatial and Temporal Satellite Imagery in the Badain Jaran Desert from 2015 to 2020

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International Journal of Crowd Science 2025年第2期9卷 96-109页

作者： Qinyu Zhao Luyan Ji Yonggang Su Kai Yu Yongchao Zhao College of Electronic and Information Engineering Hebei University Baoding China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial information Processing and Application System Beijing China Key Laboratory of Target Cognition and Application Technology (TCAT) Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

The Badain Jaran Desert is the second-largest desert in China, and its lakes, which are generally small-sized and highly dynamic, play a significant role for plants and animals in this arid region. Therefore, long-term monitoring of the distribution of lakes in the Badain Jaran Desert with high spatial and temporal resolution is of great importance. However, due to the tradeoff between pixel size and swath width, currently no single satellite sensor can provide such a time series. Thereby, in this study, we focus on applying the deep learning based spatiotemporal fusion method (super-resolution based spatial fusion with Generative Adversarial Network (GAN)) to a low spatial yet high temporal resolution data (i.e., MODIS 250 m daily reflectance time series) and a high spatial yet low temporal resolution data (i.e., Landsat 30 m 16-day reflectance time series) to generate a daily 30 m time series for 37 selected lakes in the Badain Jaran Desert. Then, an automatic water extraction algorithm is proposed, and a daily 30 m water mapping production is generated for our study area from 2015 to 2020. The overall accuracy can reach 0.92, while the average error of lake areas is less than 9.21%, which is much higher than that derived from the MODIS time series. Moreover, based on our daily high spatial resolution results, it is possible to analyze the water phenology for all sizes of lakes in the Badain Jaran Desert. We have performed a detailed analysis of interannual variability and seasonal changes for the selected 37 lakes in the Badain Jaran Desert. The results show that from 2015 to 2020, the shrinkage of the small lakes (<0.5 km 2 ) is more severe than lakes with a larger size. As for seasonal changes, the lake area can be divided into four stages: quick increase due to ice melting from winter to spring, slow decrease due to evaporation from spring to summer, moderate recovery due to the arrival of the rainy season from summer to autumn, and quick decrease due to lake

关键词： Reflectivity Heuristic algorithms Time series analysis Landsat Lakes Deserts Spatiotemporal phenomena spatial resolution MODIS Monitoring

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Interpreting the Latent Space of GANs via Measuring Decoupling

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第1期2卷 58-70页

作者： Li, Ziqiang Tao, Rentuo Wang, Jie Li, Fu Niu, Hongjing Yue, Mingdao Li, Bin The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems University of Science and Technology of China Hefei230052 China The Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230052 China The School of Information Science and Technology University of Science and Technology of China Hefei230052 China The College of Mechanical and Electrical Engineering Suzhou University Suzhou234000 China

With the success of generative adversarial networks (GANs) on various real-world applications, the controllability and security of GANs have raised more and more concerns from the community. Specifically, understanding the latent space of GANs, i.e., obtaining the completely decoupled latent space, is essential for applications in some secure scenarios. At present, there is no quantitative method to measure the decoupling of latent space, which is not conducive to the development of the community. In this article, we propose two methods to measure the sensitivity of latent dimensions: one is a sequential intervention method, and the other is an optimization-based method that measures the sensitivity in both the value and the direction. With these two methods, the decoupling of latent space can be measured by the sparsity of the sensitivity vector obtained. The effectiveness of the proposed methods has been verified by experiments on the representative GANs. Code will be available at https://***/iceli1007/latent-analysis-of. Impact Statement-Generative adversarial networks (GANs) is a popular technology in image generation. Benefiting from the development of neural networks, GANs have been widely used in many real-world tasks, such as super-resolution, image translation, and image inpainting. However, the understanding of the generator in GANs as a map from latent space (represented in a multidimensional vector) to image space is incomplete. To make better use of GANs, more insight into the generation process is required. The motivation of this article is to develop methods to analyze the influence of each dimension of latent space on the generated results, and to measure the decoupling of latent space, the knowledge obtained will be useful for the development of more precise controllable image generation technology. © 2021 IEEE.

关键词： Generative adversarial networks

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Recurrent Generic Contour-based Instance Segmentation with Progressive Learning

arXiv

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arXiv 2023年

作者： Feng, Hao Zhou, keyi Zhou, Wengang Yin, Yufei Deng, Jiajun Sun, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Zhangjiang Laboratory Shanghai China University of Adelaide Australian Institute for Machine Learning Australia

Contour-based instance segmentation has been actively studied, thanks to its flexibility and elegance in processing visual objects within complex backgrounds. In this work, we propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance with an iterative and progressive contour refinement strategy. Technically, PolySnake introduces a recurrent update operator to estimate the object contour iteratively. It maintains a single estimate of the contour that is progressively deformed toward the object boundary. At each iteration, PolySnake builds a semantic-rich representation for the current contour and feeds it to the recurrent operator for further contour adjustment. Through the iterative refinements, the contour progressively converges to a stable status that tightly encloses the object instance. Beyond the scope of general instance segmentation, extensive experiments are conducted to validate the effectiveness and generalizability of our PolySnake in two additional specific task scenarios, including scene text detection and lane detection. The results demonstrate that the proposed PolySnake outperforms the existing advanced methods on several multiple prevalent benchmarks across the three tasks. The codes and pre-trained models are available at https://***/fh2019ustc/PolySnake Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Deep Unrestricted Document Image Rectification

arXiv

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arXiv 2023年

作者： Feng, Hao Liu, Shaokai Deng, Jiajun Zhou, Wengang Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Zhangjiang Laboratory Shanghai China The University of Adelaide Australian Institute for Machine Learning Australia

In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://***/fh2019ustc/DocTr-Plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Detect Any Shadow: Segment Anything for Video Shadow Detection

arXiv

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arXiv 2023年

作者： Wang, Yonghui Zhou, Wengang Mao, Yunyao Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to consider shadows as background and therefore does not perform segmentation on them. In this paper, we propose ShadowSAM, a simple yet effective framework for fine-tuning SAM to detect shadows. Besides, by combining it with long short-term attention mechanism, we extend its capability for efficient video shadow detection. Specifically, we first fine-tune SAM on ViSha training dataset by utilizing the bounding boxes obtained from the ground truth shadow mask. Then during the inference stage, we simulate user interaction by providing bounding boxes to detect a specific frame (e.g., the first frame). Subsequently, using the detected shadow mask as a prior, we employ a long short-term network to learn spatial correlations between distant frames and temporal consistency between adjacent frames, thereby achieving precise shadow information propagation across video frames. Extensive experimental results demonstrate the effectiveness of our method, with notable margin over the state-of-the-art approaches in terms of MAE and IoU metrics. Moreover, our method exhibits accelerated inference speed compared to previous video shadow detection approaches, validating the effectiveness and efficiency of our method. The source code is now publicly available at https://***/harrytea/Detect-AnyShadow. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Plug-And-Play TomoSAR Imaging With Prior information

Plug-And-Play TomoSAR Imaging With Prior Information

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Kun Wang Jie Li Yizhe Fan Bingchen Zhang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L 1 norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.

关键词： Radar remote sensing Three-dimensional displays Morphology Tomography Apertures Convex functions Computational complexity

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Study on the three dimension imaging methods of fully-polarised array InSAR

Study on the three dimension imaging methods of fully-polari...

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IET International Radar Conference (IRC 2023)

作者： Shujie Song Xiaolan Qiu Songtao Shangguan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing People's Republic of China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou People's Republic of China

ISBN: (数字)9781837240982

Fully-polarised (FP) array interferometric Synthetic Aperture Radar (FP-Array-InSAR) is an important technology in three-dimensional (3D) reconstruction and image interpretation of various scattering mechanisms (SMs) by exploiting the structural and polarisation properties of the targets. There has been a solid foundation for multi-baseline PolInSAR, but lack of systematic comparison and analysis for FP-Array-InSAR. Relying on the UAV-borne FP-Array-InSAR system developed by our research team, this paper applies two 3D imaging methods, one based on polarisation decomposition and the other based on multi-baseline (MB) polarimetric coherence optimization, to four-channel FP-array-InSAR datasets from Suzhou, China. Finally, this paper compares and analyses the experimental results of these two methods, and then gives their applicable scenarios. Such comparative experiments are positive for the rational selection and application of different methods in FP-array-InSAR.

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SAR Moving Target Segmentation and Removal Based on Deep Learning 8th

SAR Moving Target Segmentation and Removal Based on Deep L...

引用

8th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2023

作者： Wu, Yifan Qi, Xiyu Huang, Lijia Zhang, Bingchen Yan, Lili Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China China Centre for Resources Satellite Data and Application Beijing100094 China

ISBN: (纸本)9783031717154

Synthetic Aperture Radar (SAR) stands as an integral part of advanced remote sensing technology. Nevertheless, practical applications experience inevitable disturbances from moving target noise, compromising both image integrity and target detection performance. This paper introduces a pioneering approach reliant on deep learning principles for the elimination of moving target noise within SAR imaging. Firstly, we use the Back-Projection (BP) algorithm to form the foundational images from echo signals. Employing the Unet network, we subsequently acquire a segmentation map of the moving target noise. By subtracting this segmented noise map from the original image, we succeed in the effective erasure of moving targets, yielding a resultant image devoid of moving target noise. Experimental validation demonstrates that our method can effectively remove moving target noise and yield SAR images that solely contain static scenes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Deep learning

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SFDE-net: A spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

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1-km/Daily Land Surface Temperature Optimized Dataset for the Qinghai-Tibet Plateau Based on MODIS Data(2000-2020)

全球变化数据仓储（中英文）

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全球变化数据仓储（中英文） 2023年第10期 4-7页

作者： XU Xunpeng ZHANG Yu JI Luyan TANG Hairong Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China the School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 101408China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China

Remote sensing data has strong correlation and continuity in space and time,so time series remote sensing images have low-rank *** this dataset,we repaired images using low-rank tensor ***,we preprocessed the MODIS land surface temperature data and employed spatio-temporal interpolation to initially fill in the missing values caused by cloud ***,we treated the land surface temperature time series data as a third-order spatio-temporal tensor and introduced Fourier transform on the time dimension to convert it into a space-frequency *** performing singular value decomposition and Gaussian low-pass filtering on this tensor,followed by inverse Fourier transform,we obtained a space-time ***,we further optimized the missing tensor using the alternating direction method of *** data accuracy using the method was validated through simulation experiments,where artificial masks were added and subsequently *** resulting mean absolute error(MAE)falls within the range of 2.1℃to 4.9℃.This dataset includes the following data for the Tibetan Plateau on a daily basis for the years 2000-2020:(1)the optimized surface temperature data for the cloud-shaded regions of the MOD11A1,MYD11A1 products(MOD11A1_QTP_PART,MYD11A1_QTP_PART);(2)optimized MOD11A1/MYD11A1 data(MOD11A1_QTP_TEMP,MYD11A1_QTP_TEMP);and(3)original MOD11A1 and MYD11A1 products(MOD11A1_QTP_ORIGIN,MOD11A1_QTP_ORIGIN).All data have a spatial resolution of 1 km and are stored in an integer data format,with pixel value representing the thermodynamic temperature of the surface with a scale factor of 0.02 in *** dataset is archived *** format,and consists of 43833 data files with data size of 143 GB(compressed into 21 files with 138 GB).

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