Protection of wetlands is important for ecosystem in recent years, and the classification of
wetland ground cover is the foundation of investigation and protection work. Probabilistic …

Low-rank and sparse representation (LRSR) model has gained popularity in hyperspectral
image (HSI) classification. However, most existing LRSR models are limited by the highly …

Leveraging the capabilities of traditional dictionary learning (DicL) and drawing upon the
success of deep neural networks (DNNs), the recently proposed framework of deep …

Wetland contains numerous features, and label acquisition is time-consuming, laborious,
and inaccurate. Coastal wetland land cover classification with limited labeled training …

Graph embedding is an effective method that has shown superiority in dimensionality
reduction (DR) for hyperspectral imagery (HSI) due to its ability to characterize the intrinsic …

Collaborative representation (CR) models have been widely used in hyperspectral image
(HSI) classification tasks. However, most CR classification models lack stability and …

Feature extraction and accurate classification are crucial tasks in the land-cover
classification of the hyperspectral image (HSI). We propose a guided filter (GF) of a random …