Open Access
ARTICLE
Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification
Jiaqun Zhu1, Hongda Chen2, Yiqing Fan1, Tongguang Ni1,2,*
1
Aliyun School of Big Data, Changzhou University, Changzhou, 213164, China
2
Hua Lookeng Honors College, Changzhou University, Changzhou, 213164, China
* Corresponding Author: Tongguang Ni. Email:
(This article belongs to the Special Issue: Computer Modeling for Smart Cities Applications)
Computer Modeling in Engineering & Sciences 2023, 137(3), 2267-2283. https://doi.org/10.32604/cmes.2023.027709
Received 10 November 2022; Accepted 24 March 2023; Issue published 03 August 2023
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.
Keywords
Cite This Article
APA Style
Zhu, J., Chen, H., Fan, Y., Ni, T. (2023). Transductive transfer dictionary learning algorithm for remote sensing image classification. Computer Modeling in Engineering & Sciences, 137(3), 2267-2283. https://doi.org/10.32604/cmes.2023.027709
Vancouver Style
Zhu J, Chen H, Fan Y, Ni T. Transductive transfer dictionary learning algorithm for remote sensing image classification. Comput Model Eng Sci. 2023;137(3):2267-2283 https://doi.org/10.32604/cmes.2023.027709
IEEE Style
J. Zhu, H. Chen, Y. Fan, and T. Ni "Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification," Comput. Model. Eng. Sci., vol. 137, no. 3, pp. 2267-2283. 2023. https://doi.org/10.32604/cmes.2023.027709