Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be
useful in many agroforestry applications. However, it lacks the spectral range and precision …

This article reviews the use of optical and microwave remote sensing data for soil and terrain
mapping with emphasis on applications at regional and coarser scales. Remote sensing is …

Recently, a great many deep convolutional neural network (CNN)-based methods have
been proposed for hyperspectral image (HSI) classification. Although the proposed CNN …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in
this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can …

Hyperspectral data (Table 1) is acquired as continuous narrowbands (eg, each band with 1
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …

Estimates of forest biomass are needed for various technical and scientific applications,
ranging from carbon and bioenergy policies to sustainable forest management. As local …

In this paper, we propose a new sparsity-based algorithm for automatic target detection in
hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in …

Rice means life for millions of people and it is planted in many regions of the world. It
primarily grows in the major river deltas of Asia and Southeast Asia, such as the Mekong …

Stripe noise removal (destriping) is a fundamental problem in remote sensing image
processing that holds significant practical importance for subsequent applications. These …