@Article{cmes.2023.027709,
AUTHOR = {Jiaqun Zhu, Hongda Chen, Yiqing Fan, Tongguang Ni},
TITLE = {Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2267--2283},
URL = {http://www.techscience.com/CMES/v137n3/53720},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2023.027709}
}