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The contourlet transform is a promising multiscale multidirection image representation technique emerging in recent years. Although it has been adopted in some signal and image processing areas, its application to hyperspectral image analysis has not been adequately studied. In this paper, we explore feature selection in the contourlet domain for hyperspectral classification. We apply a previously...
This paper proposed a type of data structure, Spectral Angle Sensitive Forest (SASF), which was designed for indexing and matching Hyperspectral data with spectral angle metric at low computational cost. In this paper, we theoretically and experimentally proved that this new method outperformed the traditional data structure (such as Vantage Point Tree) used for high dimensional dataset, and overcome...
The spectral variations caused by geometry and incident illumination may influence classification accuracy using spectral information alone. In this paper, spectral gradient derived from original spectral data was combined with spectral data for improved classification. The performance of spectral gradient in lithologic mapping was evaluated. Two classification methods, i.e. spectral angle mapper...
The estimation of abundance maps in hyperspectral imaging (HSI) requires the resolution of an optimization problem subject to non-negativity and sum-to-one constraints. Assuming that the spectral signatures of the image components have been previously determined by an endmember extraction algorithm, we propose here a primal-dual interior point algorithm for the estimation of their fractional abundances...
This paper presents a new approach to unsupervised classification for multispectral imagery. It first uses a Gaussian pyramid multi-resolution technique to reduce image size from which the pixel purity index (PPI) is implemented to find regions of interest (ROIs) with PPI counts greater than zero. These PPI-found samples are further used as support vectors for a support vector machine (SVM) to classify...
This study investigates the presence of surface minerals at the Dexing Copper Mine (DCM) in China through a hyperspectral Hyperion image. The Hyperion image acquired in April 2009 was utilised to map the area through spectral characteristics of three different types of minerals. The classified image identifies the degree of oxidation linked to Fe-OH absorption features. An opportunity to analyse the...
The traditional solution to addressing the small-sample-size problem as it applies to linear discriminant analysis is to implement the latter in a principal-component subspace, a strategy known as subspace linear discriminant analysis. In this work, this approach is extended by coupling subspace linear discriminant analysis and noise-adjusted principal component analysis in order to provide noise-robust...
Detection and mapping the impervious surface accurately is one of the important tasks in urban remote sensing. In this study, airborne hyperspectral data and Worldview-2 image were used to classify urban area .The main goal of this study are to compare the hyperspectral data and worldview 2 images and shows the potential of worldview 2 images for detection the impervious surface from the same area...
Traditional Orthogonal Subspace Projection (OSP) target detection method can not solve the problem of nonlinear mixing of endmember spectra. Meanwhile, Kernelized Orthogonal Subspace Projection (KOSP) method maps the inseparable data into high dimension space where the target endmembers and background endmembers can be separated. However, the background subspace remains the same for different pixels...
An estimation of bathymetry in the optically complex lagoon of New Caledonia was performed from the spectroradiometer MeRIS satellite sensor. Bathymetric estimation was obtained with different MeRIS images acquired at different dates. The method is based on the rotation of a pair of spectral bands. One of the resulting images is depth-dependent. A comparison was made with SHOM (Service Hydrographique...
This paper introduces the use of clustering algorithm using weighted features in cluster space classification of hyperspectral data. The best weights are obtained via an optimization process to seek the most compact clusters. This procedure is integrated in a cluster space classification, where the distribution of class of interest data is represented by the set of the clusters generated, instead...
In hyperspectral remote sensing, the conventional endmember extraction and unmixing procedures are often complex and associated with uncertainties. In this work, we have designed an algorithm that uses Crude Low Pass Filter (CLoPF) and Pearson's Correlation Coefficient (PCC) to identify the endmember spectra from spectral library. Subsequently, a Non-Negativity Fully Constrained Least Square (NNFCLS)...
Knowing the number of endmembers in a hyperspectral image is a prerequisite for almost all the endmember extraction algorithms and plays a key role for the accuracy of the spectral unmixing. Moreover, in case of data compression, it is important to know the number of endmembers in order to define the appropriate signal subspace. In this paper, a new automated method for estimating the number of endmembers...
We describe an in-scene method for VNIR-SWIR atmospheric correction for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction). It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the mean spectrum of a collection of diverse material...
Linear spectral unmixing is a popular tool to describe the remote sensing hyperspectral data. However, due to the huge size of hyperspectral data and the real-time processing, it needs a faster and more accurate algorithm. In this paper, we present a novel algorithm for linear spectral unmixing based on nonnegative matrix factorization (NMF), referred to as the split bregman method for NMF (SBNMF)...
Using distance geometry concepts and distance geometry constraints, this paper proposes a new abundance estimation method for hyperspectral unmixing, which improves current hyperspectral unmixing algorithms in several aspects. Firstly, considering the geometric structure of dataset by the distance geometry constraint, the optimal result with least geometric deformation can be obtained. Secondly, the...
In this paper, we select the Duobaoshan and Tongshan copper mine as the experiment area. The spectrums and samples of rock, soil and typical plants were collected in the test area, 13 metal elements in the rock, soil and plant, as well as biological parameters were all measured. It is indicated that there exists a decreasing trend for the vertical transport of elements within rock-soil-vegetation,...
Recently, we have introduced 3-D Gabor wavelets to extract the discriminative features from the hyperspectral imagery for classification. High classification accuracies have been achieved even with small training sample set. However, the computational load of the convolution operator between the original hyperspectral data and the 3-D Gabor wavelet filter is quite high. Furthermore, more than fifty...
Foliar pigment concentrations have the potential to provide information regarding the physiological status of vegetation. Since foliar pigments cause wavelength specific absorption, these spectral regions and metrics derived there from, have been applied to estimate pigment concentrations. Some literature suggests that foliar attributes not related to chlorophyll concentrations influence the reflectance-pigment...
Because of the improvement of optical remote sensing instrument, hyperspectral images now collect information of the ground with hundreds of wavelengths. This spectral information can be used to identify different materials, since each material should have its unique absorption spectrum. Traditionally the spectra was discriminated by measuring either the spectral distance or angle between two spectra...
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