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A novel interferogram quality assessment index based on connected area

A novel interferogram quality assessment index based on conn...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tao Zhang Xiaolei Lv Jun Hong Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

InSAR interferogram quality assessment is a key step for the using of interferogram map. Traditionally, the interferogram is qualitatively assessed visually and quantitatively assessed by the number of residues. However, the important structure information is hardly quantifiable. This paper presents a novel index to evaluate the quality of InSAR interferogram based on connected area. After discomposing the fringes into independent connected areas, we analyze the statistical ratio of an area to its margin. Then we use the ratio as an index to quantitatively evaluate the interferogram. In the end, the presented index is used for the filtered interferogram of popular filters, and the results fit the visual judging.

关键词： Indexes Synthetic aperture radar Remote sensing Quality assessment Noise reduction Standards Coherence

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DEEP UNFOLDING NETWORK FOR SPARSE SAR IMAGING BASED ON COMPOUND REGULARIZATION

DEEP UNFOLDING NETWORK FOR SPARSE SAR IMAGING BASED ON COMPO...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System 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

How to get an accurate image of multi-class targets scene is the key to synthetic aperture radar (SAR) imaging research. In sparse SAR imaging, compound regularization constrains multi-class target features by setting multiple regularizations prior to the model. However, the solution method based on a traditional iterative algorithm needs to set parameters manually and has a large number of iterations. In this paper, a novel SAR imaging method for the l1 norm and the total variation (TV) compound regularization based on a deep unfolding network (DUN) is proposed. The l1 norm and TV compound regularization can promote the sparsity and despeckle at the same time. Besides, the deep unfolded network can learn iteration parameters in a data-driven manner to speed up convergence. Finally, real SAR data experiments can verify the effectiveness of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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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...

引用

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

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

引用

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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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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CAN SAR IMAGES AND OPTICAL IMAGES TRANSFER WITH EACH OTHER?

CAN SAR IMAGES AND OPTICAL IMAGES TRANSFER WITH EACH OTHER?

引用

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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A new endmember extraction algorithm by maximum ASC fraction

A new endmember extraction algorithm by maximum ASC fraction

引用

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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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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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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