This paper investigates the performance of machine learning methods for classifying rock types from hyperspectral data. The main objective is to test the impact on classification ...
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. This paper introduces a new hierarchical ...
Silvia Valero, Philippe Salembier, Jocelyn Chanuss...
In this study, the authors investigate the use of hyperspectral imaging for food crop monitoring and contamination detection and characterization. The authors investigate the use ...
Terrance West, Lori M. Bruce, Saurabh Prasad, Dani...
Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral c...
In this paper, we develop a new spatial preprocessing strategy which can be applied prior to a spectral-based endmember extraction process for unmixing of hyperspectral data. Our ...
Endmember extraction is an important technique in the context of spectral unmixing of remotely sensed hyperspectral data. Winter's N-FINDR algorithm is one of the most widely...
Hyperspectral imaging segmentation has been an active research area over the past few years. Despite the growing interest, some factors such as high spectrum variability are still...
Silvia Valero, Philippe Salembier, Jocelyn Chanuss...
Maximum likelihood supervised classifications with 1-m 128 band hyperspectral data accurately map in-stream habitats in the Lamar River, Wyoming with producer's accuracies of ...
Abstract--Hyperspectral imagery affords researchers all discriminating details needed for fine delineation of many material classes. This delineation is essential for scientific re...
Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of t...