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In order to provide a careful description of the interactions among endmembers in hyperspectral images, a new method for adaptive design of mixture models for hyperspectral unmixing is introduced. Specifically, the proposed approach relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers' spectra. Then, an...
Previously proposed hyperspectral image registration methods mostly focus on the registration of the images including overlapping bands in VNIR and SWIR range. In contrary to previous methods, we investigate the registration of hyperspectral images with no-overlapping bands in MWIR and LWIR range in this paper. The proposed approach achieves the image registration over 2D maps extracted from 3D hyperspectral...
Diagnostic absorption feature has the potential to be the key factor of mineral information extraction in vegetation-covered region. Reference Spectral Background Removal (RSBR) could simulate the background curve based on the reference spectral background, and eliminate the influence through the background removal process. In this paper, RSBR was introduced into to mineral absorption feature extraction...
We report on an eight-day campaign using a longwave hyperspectral imager in NYC to observe an 8km profile of the city along the west side of Manhattan Island, from One World Trade Center to Central Park. Images were taken at roughly 3-minute intervals in 128 spectral bands from 7.4 to 13.2 microns. Results presented highlight the potential that spectroscopic imaging offers for studying both solid...
In this paper, the detection of point target in a target rich environment is considered. The standard matched filter may be problematic if the estimate of the background is contaminated by neighboring pixels. A second solution, the Orthogonal Space Projection (OSP) implementation of the Generalized Likelihood Ratio Test may be faulty due to the over-definition of the background with the use of too...
The aim of this work was to evaluate the suitability of Near-Infrared Hyperspectral Imaging Spectroscopy (NIR-HSI) for the quantification of water content in commercial biscuits. Ten biscuits from two commercial brands were conditioned for one week in desiccators with different water activities controlled by different saturated solutions of salts. Biscuits were analyzed by NIR-HSI and resulting images...
The potential hazard of heavy metals in reclaimed mine soil has influenced on the human health. The inversion analysis of hyperspectral data can be used to estimate heavy metal content of the soil effectively. In this paper, the characteristic bands are extracted by spectral pretreatment, including Savitzky-Golay (SG), Standard Normal Variety (SNV), First Derivative (FD), Second Derivative (SD), or...
The light-use efficiency (LUE) is one of critical parameters in the terrestrial ecosystem production studies. Although some published paper on the LUE in cropland had used hyperspectral data, lack of study in literature investigated the cropland LUE using the surface reflectance with high spatiotemporal and high spectral data with multiple angles. Based on the observed reflectance and flux data, a...
This paper proposes a novel system for fast and accurate content based retrieval of hyperspectral images. The proposed system aims at retrieving hyperspectral images that have both similar spectral characteristics associated with specific materials and fractional abundances to the query image. It consists of two modules. The first module characterizes the query and the target hyperspectral images...
Band selection (BS) is one of the important topics in hyperspectral image data analysis. How to search the representative bands that can effectively represent the image with lower inter-band redundancy remains an issue. This paper develops a sparse-based BS (SpaBS) method which can find those bands in a sequential order. We make an assumption that each band is a linear combination of a set of representative...
Endmember extraction plays an important role in spectral unmixing. Traditional endmember extraction algorithms, such as EEAs, only use spectral information to get the endmember, but ignore the spatial characteristic of the remote sensing image. Because of this, the algorithms are susceptible to the noise and anomaly image, which reduces the accuracy of endmember extraction. Focusing on this problem,...
Near-ground imaging spectroscopy is emerging as a promising sensing technique that can provide us with very fine spatial resolution imaging data for observing individual canopy components. It is an efficient tool to understand the within-canopy variation in spectral properties of paddy rice and resolve uncertainty sources in the spectroscopic estimation of rice chemistry. Many studies have use the...
In recent years, sparse regression has drawn much attention in hyperspectral unmixing. The well known sparse unmixing via variable splitting augmented Lagrangian (SUnSAL) and sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAl-TV) aim to find the sparsest abundance of every data vector individually. However, these methods ignore the global structure of all the vectors...
Typically, quantitative interpretation of Mars mineralogy from spectra can be retrieved by analyzing the overlaps of absorption features. It is possible to achieve a thorough description of the abundances of each mineral the considered scene is composed of by applying proper deconvolution techniques such as those based on modified Gaussian model (MGM). However, MGM-based methods are sensitive on initial...
Spatial information has shown significant contribution for hyperspectral image classification. Local Binary Pattern (LBP) can be used for extracting spatial texture features, however it is incapable of capturing textural and structural features of images at various resolution. Hence, we present a multiscale scheme on Complete LBP (CLBP) as well as on LBP to obtain better spatial features from hyperspectral...
Hyperspectral sensors produce very large images, with each pixel recorded at hundreds of different wavelengths. In this paper, a fast density peak clustering approach (FDPC), which was previously proposed for non-spherical clusters detection, is introduced to hyperspectral image classification. Since the centers generated by the unsupervised cluster algorithms may not always fit the expected classes,...
A new software have been developed for multidimensional analysis for remote sensing application. A new data storage structure named “mdd” for storing long time series remotely sensed data with spatial, temporal and spectral dimensions was induced as well as. Five data formats were included within the multidimensional data storage, which were TSB, TSP, TIB, TIP and TIS. MARS can be used for building...
In this work we propose a new deep learning tool — deep dictionary learning. We give an alternate neural network type interpretation to dictionary learning. Based on this, we build a deep architecture by cascading one dictionary after the other. The learning proceeds in a greedy fashion, therefore for each level we only need to learn a single layer of dictionary — time tested tools are there to solve...
Hyperspectral images often contain multiple intimate (nonlinear) mixtures. When attempting to unmix such datasets it is important to identify (cluster) the different mixtures present in the data and also minimize the effects of the nonlinearities in the data due to intimate mixing (embedding). Manifold clustering and embedding techniques appear to be an ideal tool for this task. Previous work in the...
The contradiction between high dimensional data and limited training samples is the main problem in hyperspectral remote sensing images classification. How to obtain high classification accuracy with limited labeled samples is an urgent issue. We propose a semisupervised classification algorithm SSP_EMP for hyperspectral remote sensing images based on spectral and spatial information. The spatial...
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