Prescreening and discrimation of maritime targets in single-channel SAR images

In this paper, we have shown an approach for the automatic colorization of SAR backscatter images, which are usually provided in the form of single-channel gray-scale imagery. Using a deep generative model proposed for the purpose of photograph colorization and a Lab-space-based SAR-optical image fusion formulation, we are able to predict artificial color SAR images, which disclose much more information to the human interpreter than the original SAR data. Future work will aim at further adaption of the employed procedure to our special case of multi-sensor remote sensing imagery. Furthermore, we will investigate if the low-level representations learned intrinsically by the deep network can be used for SAR image interpretation in an end-to-end manner.

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Water Extraction from Fully Polarized SAR Based on Combined Polarization and Texture Features

Water ◽

10.3390/w13233332 ◽

2021 ◽

Vol 13 (23) ◽

pp. 3332

Author(s):

Jikang Wan ◽

Jiayi Wang ◽

Min Zhu

Keyword(s):

Coastal Wetland ◽

Single Channel ◽

Texture Features ◽

Water Bodies ◽

Polarization Method ◽

Sar Images ◽

Research Areas ◽

Sar Data ◽

Single Polarization ◽

Occurrence Matrix

Given the limited features (for example, the backscattering coefficient threshold range) of single-channel Synthetic Aperture Radar (SAR) images, it is difficult to distinguish ground objects similar to the backscattering coefficients of water bodies. In this paper, two representative research areas are selected (Yancheng Coastal wetland and Shijiu Lake), and the fully polarized SAR data based on Gaofen-3 are used to extract water bodies using the method of polarization decomposition and gray level co-occurrence matrix. Firstly, the multi-dimensional features of ground objects are extracted, and then the redundancy processing of multi-dimensional features is carried out by the separability index, which effectively solves the misclassification of non-water bodies and water bodies and improves the accuracy of water body extraction. The comparison between the results of full-polarization extraction and single-polarization extraction shows that both full-polarization and single-polarization extraction can extract water information, but the extraction accuracy of the full-polarization method can reach 94.74% in the area with complex wetland features, which can effectively compensate for the lack of precision of the single-polarization method. Although multi-dimensional features can be extracted from fully polarimetric SAR data, data redundancy may exist. Therefore, using the Separability index (SI) to process multi-dimensional features can effectively solve the problem of feature redundancy and improve classification accuracy.

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A Filter for SAR Image Despeckling Using Pre-Trained Convolutional Neural Network Model

Remote Sensing ◽

10.3390/rs11202379 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2379 ◽

Cited By ~ 2

Author(s):

Ting Pan ◽

Dong Peng ◽

Wen Yang ◽

Heng-Chao Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Noise Level ◽

Multiplicative Noise ◽

Single Channel ◽

State Of The Art ◽

Additive White Gaussian Noise ◽

Training Data ◽

Noise Levels ◽

Sar Images

Despeckling is a longstanding topic in synthetic aperture radar (SAR) images. Recently, many convolutional neural network (CNN) based methods have been proposed and shown state-of-the-art performance for SAR despeckling problem. However, these CNN based methods always need many training data or can only deal with specific noise level. To solve these problems, we directly embed an efficient CNN pre-trained model for additive white Gaussian noise (AWGN) with Multi-channel Logarithm with Gaussian denoising (MuLoG) algorithm to deal with the multiplicative noise in SAR images. This flexible pre-trained CNN model takes the noise level as input, thus only a single pre-trained model is needed to deal with different noise levels. We also use a detector to find the homogeneous region automatically to estimate the noise level of image as input. Embedded with MuLoG, our proposed filter can despeckle not only single channel but also multi-channel SAR images. Finally, both simulated and real (Pol)SAR images were tested in experiments, and the results show that the proposed method has better and more robust performance than others.

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Statistical Modeling of Single-Channel SAR Images

Characterization of SAR Clutter and Its Applications to Land and Ocean Observations ◽

10.1007/978-981-13-1020-1_2 ◽

2019 ◽

pp. 23-73

Author(s):

Gui Gao

Keyword(s):

Statistical Modeling ◽

Single Channel ◽

Sar Images

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Radar and Optical Images Fusion Using Stripmap SAR Data with Multilook Processing

International Journal of Electronics and Telecommunications ◽

10.2478/v10177-011-0005-z ◽

2011 ◽

Vol 57 (1) ◽

pp. 37-42

Author(s):

Krzysztof Kulpa ◽

Mateusz Malanowski ◽

Jacek Misiurewicz ◽

Piotr Samczynski

Keyword(s):

Single Channel ◽

Target Recognition ◽

Real Life ◽

Sar Images ◽

Life Data ◽

The Earth ◽

Optical Images ◽

Real Life Data ◽

Image Creation ◽

Sar Data

Radar and Optical Images Fusion Using Stripmap SAR Data with Multilook Processing The paper presents the real-life data results of SAR and optical images data fusion. The fusion has been carried out for SAR images obtained in stripmap SAR mode using multilook processing with different methods of final image creation. The aim of the fusion was to enhance the target recognition capabilities on the Earth surface for a simple single-channel SAR receiver.

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