Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification

Abstract

To create a green and healthy living environment, people have put forward higher requirements for the refined management of ecological resources. A variety of technologies, including satellite remote sensing, Internet of Things, artificial intelligence, and big data, can build a smart environmental monitoring system. Remote sensing image classification is an important research content in ecological environmental monitoring. Remote sensing images contain rich spatial information and multi-temporal information, but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy. To solve this problem, this study develops a transductive transfer dictionary learning (TTDL) algorithm. In the TTDL, the source and target domains are transformed from the original sample space to a common subspace. TTDL trains a shared discriminative dictionary in this subspace, establishes associations between domains, and also obtains sparse representations of source and target domain data. To obtain an effective shared discriminative dictionary, triple-induced ordinal locality preserving term, Fisher discriminant term, and graph Laplacian regularization term are introduced into the TTDL. The triplet-induced ordinal locality preserving term on sub-space projection preserves the local structure of data in low-dimensional subspaces. The Fisher discriminant term on dictionary improves differences among different sub-dictionaries through intra-class and inter-class scatters. The graph Laplacian regularization term on sparse representation maintains the manifold structure using a semi-supervised weight graph matrix, which can indirectly improve the discriminative performance of the dictionary. The TTDL is tested on several remote sensing image datasets and has strong discrimination classification performance.