Optical/SAR Image Registration Based on Cross-Correlation with Multi-scale and Multi-direction Gabor Characteristic Matrixes

2013 ◽  
Author(s):  
Jiang Yun-hui
Keyword(s):  
Image Registration ◽  
Cross Correlation ◽  
Sar Image ◽  
Multi Scale

scholarly journals Multi-Scale Fused SAR Image Registration Based on Deep Forest

2021 ◽  
Vol 13 (11) ◽  
pp. 2227
Author(s):  
Shasha Mao ◽  
Jinyuan Yang ◽  
Shuiping Gou ◽  
Licheng Jiao ◽  
Tao Xiong ◽  
...  
Keyword(s):  
Image Registration ◽  
Multiple Scales ◽  
Binary Classification ◽  
Reference Image ◽  
Sar Image ◽  
Multi Scale ◽  
Training Samples ◽  
Key Points ◽  
Deep Forest ◽  
Matching Models

SAR image registration is a crucial problem in SAR image processing since the registration results with high precision are conducive to improving the quality of other problems, such as change detection of SAR images. Recently, for most DL-based SAR image registration methods, the problem of SAR image registration has been regarded as a binary classification problem with matching and non-matching categories to construct the training model, where a fixed scale is generally set to capture pair image blocks corresponding to key points to generate the training set, whereas it is known that image blocks with different scales contain different information, which affects the performance of registration. Moreover, the number of key points is not enough to generate a mass of class-balance training samples. Hence, we proposed a new method of SAR image registration that meanwhile utilizes the information of multiple scales to construct the matching models. Specifically, considering that the number of training samples is small, deep forest was employed to train multiple matching models. Moreover, a multi-scale fusion strategy is proposed to integrate the multiple predictions and obtain the best pair matching points between the reference image and the sensed image. Finally, experimental results on four datasets illustrate that the proposed method is better than the compared state-of-the-art methods, and the analyses for different scales also indicate that the fusion of multiple scales is more effective and more robust for SAR image registration than one single fixed scale.

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

Multi-view SAR image registration based on feature extraction

2021 ◽  
Author(s):  
JiaZheng Sun ◽  
Hui Wang ◽  
ShiChao Zheng ◽  
ZhaoYang Zeng ◽  
Xiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Sar Image
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