Design of fuzzy supervised classification system for single-channel SAR images

2002 ◽  
Author(s):  
Su Fang ◽  
Hong Wen ◽  
Mao Shiyi
Keyword(s):  
Classification System ◽  
Supervised Classification ◽  
Single Channel ◽  
Sar Images

scholarly journals Change Detection in Water reservoir using Image Fusion and Supervised Classifier

2021 ◽  
pp. 324-330
Author(s):  
Kiran Khandarkar ◽  
Dr. Sharvari Tamne
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Classification System ◽  
Supervised Classification ◽  
Water Reservoir ◽  
Input Image ◽  
Sar Images ◽  
The Mean ◽  
Log Ratio

The research provides a method for improving change detection in SAR images using a fusion object and a supervised classification system. To remove noise from the input image, we use the DnCNN denoising approach. The data from the first image is then processed with the mean ratio operator. The log ratio operator is used to process the second image. These two images are fused together using SWT-based image fusion, and the output is sent to a supervise classifier for change detection.

scholarly journals Change Detection in SAR Images using Image Fusion and Supervised Classifier

2020 ◽  
Vol 9 (1) ◽  
pp. 685-687
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Classification System ◽  
Supervised Classification ◽  
Input Image ◽  
Sar Images ◽  
Denoising Method ◽  
Pca Algorithm ◽  
Log Ratio

The paper proposes an approach based on a fusion o bject and a supervised classification system to improve detection f or SAR images. Here we are using CNN denoising method for removing noise in the input image. Then information from first image is processed using mean_ratio operator. Second image is processed by log ratio operator. These two images are fused using PCA algorithm and the output is provided to KNN supervised classifier for finding change detection in the image.

scholarly journals COLORIZING SENTINEL-1 SAR IMAGES USING A VARIATIONAL AUTOENCODER CONDITIONED ON SENTINEL-2 IMAGERY

2018 ◽  
Vol XLII-2 ◽  
pp. 1045-1051 ◽  
Author(s):  
M. Schmitt ◽  
L. H. Hughes ◽  
M. Körner ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Single Channel ◽  
Image Interpretation ◽  
Sar Image ◽  
Sar Images ◽  
Variational Autoencoder ◽  
Future Work ◽  
Special Case ◽  
Sentinel 2

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.

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

Water ◽  
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.

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

2019 ◽  
Vol 11 (20) ◽  
pp. 2379 ◽  
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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