SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Abstract

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

A Fast Matching Method for the SAR Images with Large Viewing Angles Based on Inertial Navigation Information and Neighborhood Structure Consensus

“…However, it is difficult to remove the influence of speckle noise from the complex scattering background in the SAR images, and the observation of ground targets at different time and locations also leads to great geometric distortion and different radiation information differences in the same scene. These factors lead to false matching or mismatching in the matching process of the SAR images with large viewing angles, and the matching of misjudgment points also consumes a lot of matching time [19]. In addition, the method based on SIFT is carried out with manually designed descriptors, which is difficult to adapt to the complex geometric and radiometric differences of SAR images and will lead to registration failure.…”

Monitoring of Supraglacial Lake Distribution and Full-Year Changes Using Multisource Time-Series Satellite Imagery

“…The Otsu algorithm and the Canny edge detector are commonly used for image segmentation and edge detection. The Otsu algorithm selects the threshold naturally by looking for the maximum variance between classes in the data histogram [24,25], whereas the Canny edge detector detects edge regions by Gaussian filtering and the gradient magnitude between neighbor pixels [26,27]. However, for the Canny edge detection algorithm, using a single threshold for edge detection over a large area is incomplete, whereas SAR images tend to have localized features [26,27].…”

“…The Otsu algorithm selects the threshold naturally by looking for the maximum variance between classes in the data histogram [24,25], whereas the Canny edge detector detects edge regions by Gaussian filtering and the gradient magnitude between neighbor pixels [26,27]. However, for the Canny edge detection algorithm, using a single threshold for edge detection over a large area is incomplete, whereas SAR images tend to have localized features [26,27]. In addition, SAR images of SGLs are influenced by the topography and surface environment, which cause each individual lake to have different boundary characteristics in SAR.…”