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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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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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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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CAN SAR IMAGES AND OPTICAL IMAGES TRANSFER WITH EACH OTHER?

CAN SAR IMAGES AND OPTICAL IMAGES TRANSFER WITH EACH OTHER?

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IEEE International Geoscience and Remote Sensing Symposium

作者： Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Synthetic aperture radar (SAR) and optical imaging are different remote sensing methods. Given a SAR image, is it possible to predict what the observed scene looks like in an optical image? Transfer between SAR data and optical data seems to be impossible. However, this article shows examples that by applying deep learning techniques on high resolution airborne SAR images and GoogleEarth optical images, the SAR images and optical images can transfer with each other. The transferring help us to better understand the relationship between SAR and optical image, and can be potentially used to transfer detection or classification algorithms for optical image straightforwardly to be applied on SAR image.

关键词： Image transfer between optical and SAR images Deep learning CycleGAN Generative adversarial networks Transfer learning

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Inshore Ship Detection in Sar Images Based on Deep Neural Networks

Inshore Ship Detection in Sar Images Based on Deep Neural Ne...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

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

Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.

关键词： Marine vehicles Image segmentation Synthetic aperture radar Reliability Merging Image resolution

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A new endmember extraction algorithm by maximum ASC fraction

A new endmember extraction algorithm by maximum ASC fraction

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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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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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Improved calibration method of the airborne polarimetric SAR

Improved calibration method of the airborne polarimetric SAR

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2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2011

作者： Ming, Feng Hong, Jun Zhang, Lintao Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Geo-spatial Information Processing and Application System Technology CAS Beijing 100190 China National Key Laboratory of Microwave Imaging Technology Beijing 100190 China Graduate University of the Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)9788993246179

The PolSAR system must be calibrated before obtain effective polarimetric information. The airplane attitude is unstable during the PolSAR measurement, which will impact the polarimetric calibration precision. This paper improved the polarimetric calibration method proposed by Whitt considering the impact of airplane attitude. The improved method is proved by polarimetric experiment using a PolSAR system developed by IECAS(Institute of electronics, Chinese Academy of Sciences). © 2011 KIEES.

关键词： Calibration

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引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Feng Wang Yuming Xiang Xinghui Yao Jiayin Liu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

The conventional shape similarity measurements of remote sensing data face problems in the situation of noise interference, partial information occlusion and missing. A method of shape similarity measurement based on principal curvature enhancement distance transformation is proposed. The distance transformation is carried out to extend the range of the shape contour, improving the robustness of the similarity measure. Besides, to ensure the accuracy of measurement results, the distance map is enhanced by the principal curvature of the shape contour, improving the response of contours with rich information. application experiments of road vectors with GPS data and optical remote sensing images show that the method is effective in practical application.

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