In recent years, deep-learning techniques revolutionized the way remote sensing data are
processed. The classification of hyperspectral data is no exception to the rule, but it has …

A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

Many fields such as data science, data mining suffered from the rapid growth of data volume
and high data dimensionality. The main problems which are faced by these fields include …

Deep neural networks have been widely applied to hyperspectral image (HSI) classification
areas, in which recurrent neural network (RNN) is one of the most typical networks. Most of …

This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …

Coastal wetlands are main components of the “blue carbon” ecosystems in coastal zones.
Salt-marsh biomass is especially important regarding climate-change mitigation. Generating …

Several versions of convolutional neural network (CNN) were developed to classify
hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral …

Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …

Hyperspectral imaging is becoming an increasingly popular tool for high-throughput plant
phenotyping, because it provides remarkable insights about the health status of plants …

Dense time-series remote sensing images have transformed the traditional bitemporal land-
cover change detection to continuous monitoring. Previous work mostly employs linear …