Abstract

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.

Highlights

  • Synthetic Aperture Radar (SAR) [1,2] is of the characteristics of all-day, all-weather, high-resolution imaging, compared with infrared imaging and optical imaging

  • We will validate the performance of the proposed method from several items: (1) The comparison performance of our method with the state-of-the-art methods on four datasets of SAR image registration; (2) the visualization on the chessboard diagram of SAR image registration; (3) the analysis on the performance obtained based on different scales

  • The size of two images is 400 × 400 and the resolution is 10 m. Both YellowR1 Data and YellowR2 Data were obtained by the Radarsat-2 satellite at Yellow River of China, and their two SAR images were obtained on June 2008 and June 2009, respectively

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Summary

Introduction

Synthetic Aperture Radar (SAR) [1,2] is of the characteristics of all-day, all-weather, high-resolution imaging, compared with infrared imaging and optical imaging. Due to the specificity of the SAR imaging mechanism, its speckle noise has an impact on the performance of the area-based and feature-based methods This indicates that the traditional image registration techniques are insufficient to provide preciser auxiliary for other problems of SAR image processing. Most studies of DL-based SAR image registration regard image patches with a fixed size as one sample to represent a matching point in general, whereas the information contained in patches with different sizes may be diverse in practice; an illustration is shown, where the left image is the reference image and the right is the sensed image. The self-learning method is firstly utilized to generate pair matching and non-matching image blocks with multiple scales based on the key points of the reference image and its transformed image, and the generated pair image blocks are used to construct multi-scale training sets.

The Proposed Method
Deep Forest
Constructing Multi-Scale Training Sets
Training Matching Model
Experimental Results and Analyses
Experimental Data and Settings
The Comparison Performance
The Visualization on SAR Image Registration
Analyses on Registration Performance with Different Scales
Discussion
Running Time
An Application on Change Detection
Conclusions
Full Text
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