Local Deep Descriptor for Remote Sensing Image Feature Matching

作者：Yunyun Dong, Weili Jiao, Tengfei Long, Lanfa Liu, Guojin He, Chengjuan Gong, Yantao Guo · 发表于：Remote Sensing · 年份：2019 · DOI：10.3390/rs11040430 · 被引用次数：49 · 研究领域：Advanced Image and Video Retrieval Techniques、Robotics and Sensor-Based Localization、Advanced Neural Network Applications

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate...