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Workshop on Hyperspectral Image and Signal processing: Evolution in Remote Sensing, WHISPERS

作者： Luyan Ji Xiurui Geng Yongchao Zhao Key Laboratory of Technology in Geo-spatial information Processing and Application System Chinese Academy of Science Beijing China

This paper presents a new simplex-based method for unsupervised endmember extraction, called maximum abundance sum-to-one constraint (ASC) fraction (MAF). The ASC fractions refer to the spectral unmixing results with the abundance sum-to-one constraint unmixing only. The algorithm assumes the existence of the pure pixels in the input data for every endmember in the scene, and exploits the fact that pixels with maximum ASC fractions are corresponding to the vertices of a simplex. In order to demonstrate the performance of the proposed MAF, the N-findr algorithm (N-FINDR) and vertex component analysis (VCA) based merely on PCA dimensional reduction are used for comparison. Experiments using both simulated and real hyperspectral data show that MAF is effective in searching optimal results, with a low computational complexity.

关键词： Abstracts Complexity theory

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SAR TARGET CLASSIFICATION WITH CYCLEGAN TRANSFERRED SIMULATED SAMPLES

SAR TARGET CLASSIFICATION WITH CYCLEGAN TRANSFERRED SIMULATE...

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IEEE International geoscience and Remote Sensing Symposium

作者： Lei Liu Zongxu Pan Xiaolan Qiu Lingxiao Peng Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Target classification is an important part in automatic target recognition (ATR) systems. Deep learning methods get state of the art performance in SAR target classification. Simulation is a useful data augmentation method when the numbers of real samples for training is not sufficient. This article discusses how to release the full potential of simulated samples which is used to improve performance of SAR target classifier. The proposed method is based on cycle adversarial network (CycleGAN), which can transfer simulated samples to be more similar with real samples in image domain. Experiments show that adding simulated samples straightforward into training dataset is not helpful to improve the performance. However, adding the transferred simulated samples for training results in about 10% increase in accuracy in the designed SAR airplane classification experiment, compared with training without data augmentation.

关键词： Training Testing Synthetic aperture radar Azimuth Gallium nitride Data models Airplanes

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SUPER-RESOLUTION OF REMOTE SENSING IMAGES BASED ON TRANSFERRED GENERATIVE ADVERSARIAL NETWORK

SUPER-RESOLUTION OF REMOTE SENSING IMAGES BASED ON TRANSFERR...

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IEEE International geoscience and Remote Sensing Symposium

作者： Wen Ma Zongxu Pan Jiayi Guo Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Single image super-resolution (SR) has been widely studied in recent years as a crucial technique for remote sensing applications. This paper proposes a SR method for remote sensing images based on a transferred generative adversarial network (TGAN). Different from the previous GAN-based SR approaches, the novelty of our method mainly reflects from two aspects. First, the batch normalization layers are removed to reduce the memory consumption and the computational burden, as well as raising the accuracy. Second, our model is trained in a transfer-learning fashion to cope with the insufficiency of training data, which is the crux of applying deep learning methods to remote sensing applications. The model is firstly trained on an external dataset DIV2K and further fine-tuned with the remote sensing dataset. Our experimental results demonstrate that the proposed method is superior to SRCNN and SRGAN in terms of both the objective evaluation and the subjective perspective.

关键词： Remote sensing images Super-resolution Generative adversarial network Transfer learning

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Automatic Registration of Optical and SAR Images VIA Improved Phase Congruency

Automatic Registration of Optical and SAR Images VIA Improve...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Yuming Xiang Rongshu Tao Feng Wang Hongjian You Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Owing to the property of being constant to image contrast and the identification of various types of features, phase congruency (PC) model has been widely used in remote sensing applications. However, when the PC is directly applied to optical and synthetic aperture radar (SAR) image registration, it fails to handle large radiometric and geometric differences. In this paper, we propose an automatic algorithm to solve this problem. First, evenly-distributed keypoints are extracted from the optical images via the block harris method. Complementary grid points are selected in image regions with poor structure and texture information. Then a robust similarity metric based on the improved PC model is proposed. Since the two images show diverse properties, we utilize two different PC models, the traditional PC and the SAR-PC. The PC values of several directions are aggregated to construct the feature descriptors on the basis of which, as a result, a similarity metric using the normalized correlation coefficient (NCC) is obtained. We compare the proposed metric with two baselines (mutual information and NCC) and a state-of-the-art method (histogram of the oriented phase congruency, HOPC) in the case of various scenarios, the results show that our method outperforms the baselines and show comparable performance with HOPC in regions with abundant structure information and better performance in untextured regions.

关键词： Optical imaging Adaptive optics Radar polarimetry Optical sensors Measurement Feature extraction Image edge detection

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SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

arXiv

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arXiv 2022年

作者： Liu, Jianzhao Li, Xin Peng, Yanding Yu, Tao Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China

Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed with diverse distortion types and degrees. To warp the compressed images into the shared representation space while maintaining the complex distortion information, we extract the hierarchical feature representations from each stage of the Swin Transformer. Besides, we utilize cross attention operation to map the extracted feature representations into a learned Swin distance space. Experimental results show that the proposed metric achieves higher consistency with human's perceptual judgment compared with both traditional methods and learning-based methods on CLIC datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Image compression

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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 application of the Alternate Descent Conditional Gradient Method in Tomographic SAR Off-grid Imaging

The Application of the Alternate Descent Conditional Gradien...

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IET International Radar Conference 2023, IRC 2023

作者： Shao, Mingxiao Su, Cikang Zhang, Zhe Zhang, Bingchen The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Compressed sensing (CS) technology has been applied to topographic synthetic aperture radar (TomoSAR) imaging due to the sparsity of elevation signals. The traditional CS algorithms discretize the elevation into many grids and assume that the scatterers are located on grids, therefore, the accuracy of the traditional CS algorithms is limited by the off-grid effect. Although there are currently methods to address the off-grid effect, such as atomic norm minimization (ANM), its application is limited by the baseline distribution form of TomoSAR, which only applies to uniformly observed scenarios. Alternating Descent Conditional Gradient (ADCG) has been applied in many fields as a gridless imaging algorithm. In this paper, we apply ADCG to TomoSAR imaging for the first time, achieving the reconstruction of non-uniform baseline observation scenes. The effectiveness of ADCG in TomoSAR imaging was verified by comparing it with iterative soft thresholding (IST) and orthogonal matching pursuit (OMP) through simulation experiments. © The Institution of Engineering & technology 2023.

关键词： Compressed sensing

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Dominant Physical Scattering Mechanism Analysis for GF-3 Typical Ground Objects by Polarimetric Decomposition

Dominant Physical Scattering Mechanism Analysis for GF-3 Typ...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Jin Yan Qiu Xiaolan Lijia Huang Laboratory of Spatial Information Intelligent Processing System Institute of Electronics Chinese Academy of Sciences Suzhou China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS

The dominant scattering mechanism is of great significance for the application of ground objects classification and target detection. It can also verify the quality of the polarimetric data by check the dominant scattering mechanism of known ground objects. In order to improve the application performance, this paper studies the dominant scattering mechanism of GF-3 typical ground objects based on a large number of data slices. The GF-3 fully polarimetric data slices are classified based on the MODIS global classification map, and the GF-3 slice library of typical ground objects is constructed. Based on large amounts of GF-3 samples, we carry out the statistical analysis of dominant scattering mechanism separation results for typical GF-3 ground objects (building, woodland, cultivated land, grassland and waters) of by means of h/alpha/A decomposition. The quantitative results reveal the polarimetric scattering feature of different ground objects, and provide reference for fully polarimetric SAR application.

关键词： Scattering Synthetic aperture radar Entropy Buildings Statistical analysis Radar polarimetry Sensors

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Curved-path SAR geolocation error analysis based on BP algorithm 38

Curved-path SAR geolocation error analysis based on BP algor...

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38th Annual IEEE International geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Liu, Junbin Qiu, Xiaolan Huang, Lijia Ding, Chibiao Liu, Ming University of Chinese Academy of Sciences China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS China Institute of Electronics Chinese Academy of Sciences China National Disaster Reduction Center Ministry of Civil Affairs NDRCC China

ISBN: (纸本)9781538671504

The theoretical modeling and analysis of SAR location error play an important role in SAR system design and error source budget. Existing SAR geolocation error models are mainly implicit, which are not easy to do analysis, especially in the curved-path case. In this paper, a theoretical explicit model of the relationship between image geolocation error and the path measurement error is established for curved-path SAR, based on BP imaging algorithm. Simulations are given which verify the correctness of the model. The explicit model and the analysis results provide an effective reference for understanding and budgeting the system-level geometric location error for curved-path SAR, such as geoSAR. © 2018 IEEE

关键词： geology Imaging Synthetic aperture radar Trajectory Mathematical model Analytical models Simulation

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Object-based method for optical and SAR images change detection

Object-based method for optical and SAR images change detect...

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IET International Radar Conference 2018, IRC 2018

作者： Wan, Ling Zhang, Tao You, Hongjian Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China Institute of Electronics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100039 China

This study introduces an automatic method for change detection of multi-sensor remote-sensing images (e.g. optical and synthetic aperture radar (SAR) images). As object-based image analysis can effectively reduce the spurious changes and the sensitivity to registration, first, multi-date segmentation is employed to generate homogeneous image objects in spectral, spatial, and temporal, in order to weak the intensity variation effects of multi-sensor images. Then, modified fuzzy c-means (FCM) algorithms are employed to preliminarily classify optical and SAR images, and a criterion is defined using membership values of parcels to select the sample parcels for each class and image. Finally, a change detection principle, which takes statistical properties as the feature space, is introduced to detect changes between multi-sensor images. The experiment results verify that the proposed method is able to cope with optical and SAR images change detection. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Change detection

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