Tensor Representation and Manifold Learning Methods for Remote Sensing Images

TL;DR

This paper explores advanced machine learning techniques, including tensor and manifold learning, to develop efficient algorithms for automatic interpretation of high-resolution remote sensing images.

Contribution

It unifies manifold learning, tensor methods, sparse learning, and transfer learning into a single framework for remote sensing image analysis.

Findings

Improved accuracy in remote sensing image classification

Efficient algorithms for large-scale RS data processing

Unified framework enhances interpretability and performance

Abstract

One of the main purposes of earth observation is to extract interested information and knowledge from remote sensing (RS) images with high efficiency and accuracy. However, with the development of RS technologies, RS system provide images with higher spatial and temporal resolution and more spectral channels than before, and it is inefficient and almost impossible to manually interpret these images. Thus, it is of great interests to explore automatic and intelligent algorithms to quickly process such massive RS data with high accuracy. This thesis targets to develop some efficient information extraction algorithms for RS images, by relying on the advanced technologies in machine learning. More precisely, we adopt the manifold learning algorithms as the mainline and unify the regularization theory, tensor-based method, sparse learning and transfer learning into the same framework. The main contributions of this thesis are as follows.