Hyperspectral imaging is an important tool in remote sensing, allowing for accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a hyperspectral image rarely represents a single material, but rather a mixture of different spectra. Hyperspectral Unmixing (HSU) aims at estimating the pure spectra present in the scene of interest, referred to as endmembers, and their fractio...

High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spe...

BACKGROUND Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a v...

This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying urban andscape changes in Indianapolis, Indiana, the United States, and for examining the environmental impact of such changes on land urface temperatures (LST). Three dates of Landsat TM/ETM+ images, acquired in 1991, 1995, and 2000, respectively, were tilized to document the hi...

Building extraction from imagery has been an active research area for decades. However, the precise building detection from hyperspectral (HSI) images solely is a less often addressed research question due to the low spatial resolution of data. The building boundaries are usually represented by spectrally mixed pixels, and classical edge detector algorithms fail to detect borders with sufficien...

—Hyperspectral imagery from EO-1 Hyperion and AVIRIS were used in conjunction with continuous forest inventory (CFI) data to map detailed forest composition in the state forests of Western Maryland. We developed a hierarchical vegetation classification that conformed to the National Vegetation Classification Standard (NVCS) at the Alliance level and mapped these forest types as a function of hy...

Developments in space-based hyperspectral sensors, advanced remote sensing, and machine learning can help crop yield measurement, modelling, prediction, monitoring for loss prevention global food security. However, precise continuous spectral signatures, important large-area growth early prediction of production with cutting-edge algorithms, be only provided via imaging. Therefore, this article...

Hyperspectral remote sensing allows for the detailed analysis of the surface of the Earth by providing high-dimensional images with hundreds of spectral bands. Hyperspectral image classification plays a significant role in hyperspectral image analysis and has been a very active research area in the last few years. In the context of hyperspectral image classification, supervised techniques (whic...

Data simulation is widely used in remote sensing to produce imagery for a new sensor in the design stage, for scale issues of some special applications, or for testing of novel algorithms. Hyperspectral data could provide more abundant information than traditional multispectral data and thus greatly extend the range of remote sensing applications. Unfortunately, hyperspectral data are much more...