With the rapid increase in the volume of data on the aquatic environment, machine learning
has become an important tool for data analysis, classification, and prediction. Unlike …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

Contrastive learning methods have significantly narrowed the gap between supervised and
unsupervised learning on computer vision tasks. In this paper, we explore their application …

Hyperspectral image (HSI) and multispectral image (MSI) fusion, which fuses a low-spatial-
resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a …

Hyperspectral image classification (HSIC) is essential in remote sensing image analysis.
Applying a graph neural network (GNN) to hyperspectral image (HSI) classification has …

In this paper, we propose a novel ensemble and robust anomaly detection method based on
collaborative representation-based detector. The focused pixels used to estimate the …

Domain adaptation aims to exploit the supervision knowledge in a source domain for
learning prediction models in a target domain. In this article, we propose a novel …

High-dimensional and complex spectral structures make the clustering of hyperspectral
images (HSIs) a challenging task. Subspace clustering is an effective approach for …

Road segmentation from remote sensing images is a critical task in many applications. In
recent years, various approaches, particularly deep learning-based methods, have been …

Hyperspectral image (HSI) contains complex multiple structures. Therefore, the key problem
analyzing the intrinsic properties of an HSI is how to represent the structure relationships of …