The purpose of this study is to validate the accuracy of abundance map reference data (AMRD) for three airborne imaging spectrometer (IS) scenes. AMRD refers to reference data maps (“ground truth”) that are specifically designed to quantitatively assess the performance of spectral unmixing algorithms. While classification algorithms typically label whole pixels as belonging to certain ground co...

Integrating spatial information into hyperspectral unmixing procedures has been shown to have a positive effect on the estimation of fractional abundances due to the inherent spatial–spectral duality in hyperspectral scenes. However, current research works that take spatial information into account are mainly focused on the linear mixing model. In this paper, we investigate how to incorporate s...

Nonnegative Matrix Factorization (NMF) is an unsupervised learning method that has been already applied to many applications of spectral signal unmixing. However, its efficiency in some applications strongly depends on optimization algorithms used for estimating the underlying nonnegatively constrained subproblems. In this paper, we attempt to tackle the optimization tasks with the inexact Inte...

: Aiming at the disadvantage of hard per-parcel classification which can't solve the difficulty of mixed parcel resulting in the low accuracy, a new method of soft per-parcel classification is presented, that is linear mixed parcel unmixing. Based on the linear spectral theory for the parcel unmixing, the predicted fraction value is assigned to a parcel. The RMSE results show that the accuracy ...

Hyperspectral imaging is a promising non-invasive method for applications in conservation of painting. With its ability to capture both spatial and spectral information which relates to physical characteristics of materials, the identification of pigments and its spatial distribution across the painting is now possible. In this work, The Scream (1893) by Edvard Munch is acquired using a hypersp...

Given this model, spectral unmixing and endmember detection are the tasks of determining the endmembers and the proportions for every data point in the scene. Several endmember detection and spectral unmixing algorithms have been developed in the literature. However, the majority of these methods do not provide an autonomous way to estimate the number of endmembers and, thus, require the number...

A new approach to multispectral and hyperspectral image analysis is presented. This method, called convex cone analysis (CCA), is based on the fact that some physical quantities such as radiance are nonnegative. The vectors formed by discrete radiance spectra are linear combinations of nonnegative components, and they lie inside a nonnegative, convex region. The object of CCA is to find the bou...

local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. the current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. the present study was devoted t...

Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Indepe...

4 We present two new linear algorithms that perform unmixing in hyper-spectral 5 images and then recognize their targets whose spectral signatures are given. The 6 first algorithm is based on the ordered topology of spectral signatures. The second 7 algorithm is based on a linear decomposition in each pixel’s neighborhood. The sought 8 after target can occupy subor above pixel. These algorithms...