A Matching Optimization Algorithm About Low-Altitude Remote Sensing Images Based on Geometrical Constraint and Convolutional Neural Network

Abstract

This article presents a novel matching optimization algorithm for low-altitude remote sensing images based on a geometrical constraint and a convolutional neural network (CNN). The proposed method was designed to be effective in enhancing the integrity and accuracy of point clouds generated by stereo matching. To overcome the limitations of stereo matching, we trained a CNN to predict how well image patches match and used it in patch optimization. The main advantage of this approach is that the proposed algorithm can decrease the mismatching and errors caused by noise, deep discontinuity, and weak texture in low-altitude remote sensing images and can reconstruct an integrated and accurate point cloud. Comparative studies and experimental results validate the accuracy of the proposed algorithm when used for dense point generation from low-altitude remote sensing images.