In recent years, patchwise classification methods are commonly adopted when dealing with
the hyperspectral image (HSI) classification. Despite their promising results from the …

Due to the proliferation of large-scale remote-sensing (RS) archives with multiple
annotations, multilabel RS scene classification and retrieval are becoming increasingly …

Due to the fast inference and good performance, convolutional neural network (CNN) has
been widely applied in image denoising. Some new approaches, such as residual learning …

Deep learning-based hyperspectral image (HSI) classification methods have recently
attracted significant attention. However, features captured by convolutional neural network …

Semisupervised learning (SSL) methods have been proved to be effective at solving the
labeled samples shortage problem by using a large number of unlabeled samples together …

Nonlinear kernel-based subspace clustering methods that can reveal the multi-cluster
nonlinear structure of samples are an emerging research topic. However, the existing kernel …

In the past few years, deep learning-based methods have shown commendable
performance for hyperspectral image (HSI) classification. Many works focus on designing …

While supervised learning techniques have demonstrated state-of-the-art performance in
many medical image analysis tasks, the role of sample selection is important. Selecting the …

In hyperspectral image classification (HSIC), background (BKG) is generally excluded from
consideration due to the fact that obtaining complete knowledge of BKG is nearly impossible …

Currently, deep neural networks (DNNs) are an important method for handling hyperspectral
image (HSI) classification because of their good performance in image processing …