Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …

In this paper, we propose a spectral–spatial feature based classification (SSFC) framework
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …

Saliency detection has been a hot topic in recent years, and many efforts have been devoted
in this area. Unfortunately, the results of saliency detection can hardly be utilized in general …

Soil salinity is a widespread environmental hazard and the main causes of land degradation
and desertification, especially in arid and semi-arid regions. The first step in finding such a …

In recent years, dimensionality reduction (DR) and classification have become important
issues of hyperspectral image analysis. In this paper, we propose a new spatial–spectral …

Climate change and anthropopression significantly impact plant communities by leading to
the spread of expansive and alien invasive plants, thus reducing their biodiversity. Due to …

Dimensionality reduction (DR) is a widely used technique to address the curse of
dimensionality when high-dimensional remotely sensed data, such as multi-temporal or …

This letter presents a novel deep learning algorithm for feature extraction from the
hyperspectral images. The proposed method takes advantage of the knowledge that the …

One of the challenging problems in processing high dimensional data, as hyperspectral
images, with better spectral and temporal resolution is the computational complexity …

Hyperspectral image is composed of many spectral bands. Due to this reason many
problems crop up in the picture. The Presence of high dimension, information loss, clinging …