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Soil Organic Carbon (SOC) is a key soil property and is important for understanding carbon storage and soil-vegetation dynamics. Hyperspectral imagery (imaging spectroscopy) providing detailed spectral signatures of vegetation and soil make it possible to continuously map SOC content over a watershed scale. In this paper, the Next Generation Airborne Visible / Infrared Imaging Spectrometer (AVTPJSng)...
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...
In this paper, different temperature and emissivity separation (TES) for hyperspectral thermal infrared data, including iterative spectrally smooth temperature-emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI), and linear spectral...
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,...
Multiple kernel learning (MKL) is an elegant tool for heterogeneous fusion. In support vector machine (SVM) based classification, MK is a homogenization transform and it provides flexibility in searching for high-quality linearly separable solutions in the reproducing kernel Hilbert space (RKHS). However, performance often depends on input and kernel diversity. Herein, we explore a new way to extract...
Spatial resolution is one of the most important assessments to evaluate an image. Enhancing spatial resolution consequently becomes a hot issue. As is all known, multispectral (MS) image, which is widely studied in remote sensing (RS) field, can be fused with the corresponding high-resolution panchromatic image to promote spatial-quality. In this paper, we consider the question regarding how to enhance...
In hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually suppose that a unique spectral signature, called an endmember, can be associated with each pure material present in the scene. This assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the composition,...
Mixed pixels in remotely sensed imagery degrade its value in practical use. Sub-pixel mapping is a promising technique to solve this problem. It can generate a fine resolution land cover map from coarse resolution fractional images by predicting spatial locations of land cover classes at sub-pixel scale. However, accuracy is often limited. When the scale factor is large, the sub-pixel distribution...
Hyperspectral remote sensing image (HSI) consists of hundreds of bands that contain rich space, radiation and spectral information. The high-dimensional data can also lead to the course of dimensionality problem making it difficult to be used effectively. In this paper, we proposed a manifold learning algorithm to reduce the dimensionality for HSI data. For high dimensional datasets with continuous...
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...
The standard matched subspace detector is modified by replacing a full-rank oblique pseudoinverse with a reduced-dimension version that trades bias for variance. Minimization of the bias squared plus variance produces an order determination rule relating signal to noise ratio and noise gain. The reduced dimension detector is then applied to several detection tasks in hyperspectral imagery yielding...
Several ammonia emitting fumarole fields have recently been exposed along the southeastern shoreline of the Salton Sea in Imperial County, California. A complex assemblage of sulfate minerals, many containing ammonium ion, are associated with the fumaroles. The distribution of these sulfates was mapped by remote sensing with the Mako LWIR hyperspectral sensor. The most common minerals tended to form...
Hyperspectral data provide indispensable timely information for environmental monitoring. It has become one of the most sought after data set for many specific applications. However, for large areal coverage, spaceborne hyperspectral data are currently acquired at low resolution. Due to the proven usefulness of hyperspectral data and its potential in newer applications, many researchers have investigated...
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...
Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex nonlinear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning...
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...
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