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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Predictive auto-encoding network for blind stereoscopic image quality assessment

arXiv

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arXiv 2019年

作者： Xu, Jiahua Zhou, Wei Chen, Zhibo Ling, Suiyi Le Callet, Patrick CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China quipe Image Perception et Interaction Laboratoire des Sciences du Numrique de Nantes Universit de Nantes Nantes44300 France

Stereoscopic image quality assessment (SIQA) has encountered non-trivial challenges due to the fast proliferation of 3D contents. In the past years, deep learning oriented SIQA methods have emerged and achieved spectacular performance compared to conventional algorithms which are only relied on extracting hand-crafted features. However, most existing deep SIQA evaluators are not specifically built for stereoscopic content and consider little prior domain knowledge of the 3D human visual system (HVS) in network design. In this paper, to better simulate the binocular rivalry phenomenon, we propose a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality assessment (NR-SIQA). The proposed encoder-decoder architecture is inspired by the predictive coding theory that the cognition system tries to match bottom-up visual signal with top-down predictions. Besides, we design the Siamese framework to mimic the binocular rivalry in the 3D HVS based on the likelihood and prior maps generated from the predictive coding process. Extensive experimental results on three publicly available stereoscopic image quality databases demonstrate that the devised approach outperforms state-of-the-art algorithms for predicting the perceptual quality of both symmetrically and asymmetrically distorted stereoscopic images with various distortion types. Copyright © 2019, The Authors. All rights reserved.

关键词： Network coding

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Aircraft Detection in Sar Images Using Saliency Based Location Regression Network

Aircraft Detection in Sar Images Using Saliency Based Locati...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wenhui Diao Fangzheng Dou Kun Fu Xian Sun Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China

In this paper, a novel framework for aircraft detection in high resolution apron area in Synthetic Aperture Radar (SAR) images is proposed, which combines the strength of location regression based convolutional neural network (CNN) framework and the salient features of target in SAR images. Specifically, a Constant False Alarm Rate (CFAR) based target pre-locating algorithm is introduced, which can match the scale of target in SAR images more accurate compared to the existing region proposal method. In addition, in order to eliminate the fact of overfitting, we explore several strategies for SAR data augmentation, including translation, adding noise and rotation within a small range. Experiments are conducted on the data set acquired by the TerraSAR-X satellite in a resolution of 3.0 meters. The results show that the proposed detection framework could effectively obtain a more accurate detection result.

关键词： Synthetic aperture radar Aircraft Object detection Training Image resolution Radar imaging Atmospheric modeling

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Dem Extraction Using C-Band Circular Sar Data

Dem Extraction Using C-Band Circular Sar Data

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shanshan Feng Yun Lin Wen Hong Bing Han Yanping Wang Yanhui Yang Wenjie Shen Fei Teng Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China North China University of Technology Beijing China Beijing Institute of Remote Sensing Equipment Beijing China

Circular Synthetic Aperture Radar(CSAR) has become a hotspot with its characteristic of elevation plane resolution and all-aspect observing ability. Digital elevation model (DEM) extraction in urban arears by using single-pass CSAR data without requiring additional knowledge is a subject of interest. The target, whose real height is not equal to the reference imaging height will project to different locations after imaging in different sub-aperture. In this paper, the quantitative relationship between offset of imaging points and height difference is deduced theoretically in the real scene, where the airborne SAR platform trajectory is not a standard circle. DEM of an area is presented using the data acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS). Compared with the DEM provided by the German Aerospace Center (DLR) with 1m absolute height error, the effectiveness of the proposed method is verified.

关键词： Synthetic aperture radar Imaging Azimuth Trajectory Radar polarimetry Data mining Geometry

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InSAR DEM Reconstruction Based on Backprojection Algorithm in Two Converse Flights

InSAR DEM Reconstruction Based on Backprojection Algorithm i...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Xiaoning Hu Maosheng Xiang Bingnan Wang Xikai Fu University of Chinese Academy of Sciences National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing China National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distortions such as shadow and layover in the mountainous terrain, which will reduce the quality of generated DEM. Fusion of two or more different aspects of InSAR data can deal with this problem. We propose an InSAR DEM reconstruction method based on backprojection (BP) algorithm in two converse flights. This method utilizes the feature of BP algorithm that geocoding has been realized in imaging process to simplify the fusion process of multi-aspect InSAR data. In addition, an iterative DEM extraction method is introduced to improve DEM accuracy. Experimental results verify the effectiveness of the proposed method.

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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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AUTOMATIC WATER DETECTION METHOD IN FLOODING AREA FOR GF-3 SINGLE-POLARIZATION DATA

AUTOMATIC WATER DETECTION METHOD IN FLOODING AREA FOR GF-3 S...

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IEEE International Geoscience and Remote Sensing Symposium

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

China is a flood disaster-prone country, floods occur almost every year, especially in July and August. Rapid detection and assessment for floods affected areas are of great significance. The Chinese GF-3 SAR satellite, which uses active ground observation technology, has obvious advantages in flood disaster monitoring owing to its all-day, all-weather imaging characteristics. For the purpose of rapid water detection in flooding area, an automatic detection method of flood area based on GF-3 single-polarization SAR data is proposed. The proposed method consists of image preprocessing and water extraction. The experimental results show that the proposed method can realize rapid and accurate extraction of waters in flood disaster area.

关键词： Floods Synthetic aperture radar Data mining Imaging Satellites Manuals Urban areas

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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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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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