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This paper proposes a classification approach for hyperspectral image (HSI) using the local receptive fields based kernel extreme learning machine. Extreme learning machine (ELM) has drawn increasing attention in the pattern recognition filed due to its simpleness, speediness and good generalization ability. A kernel method is often used to promote ELM's performance, which is known as kernel ELM....
We propose a supervised approach to the classification and segmentation of material regions in hyperspectral imagery. Our algorithm is a two-stage process, combining a pixelwise classification step with a segmentation step aiming to minimise the total perimeters of the resulting regions. Our algorithm is distinctive in its ability to ensure label consistency within local homogeneous areas and to generate...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Nonnegative matrix factorization (NMF) based hyperspectral unmixing aims at estimating pure spectral signatures and their fractional abundances at each pixel. During the past several years, manifold structures have been introduced as regularization constraints into NMF. However, most methods only consider the constraints on abundance matrix while ignoring the geometric relationship of endmembers....
Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data...
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions — by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate...
We propose a novel predictive lossless compression algorithm for regions of interest (ROIs) in the hyperspectral images via Maximum Correntropy Criterion (MCC) based Least Mean Square (LMS) filtering. Non-linearity and non-Gaussian conditions of prediction residuals of the ROI pixels in the hyper-spectral image are taken into account to improve the compression performance compared to the ordinary...
We apply social ℓ-norms for the first time to the problem of hyperspectral unmixing while modeling spectral variability. These norms are built with inter-group penalties which are combined in a global intra-group penalization that can enforce selection of entire endmember bundles; this results in the selection of a few representative materials even in the presence of large endmembers bundles capturing...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
This paper proposes a novel multi-dimensional morphology descriptor, tensor morphology profile (TMP), for hyperspectral image classification. TMP is a general framework to extract the multi-dimensional structures in high-dimensional data. The nth-order morphology profile is proposed to work with the nth-order tensor, which can capture the inner high order structures. This is different with the traditional...
Classification plays a significant role in analyzing remotely sensed imagery. In order to obtain an optimized classier, following aspects are rather challenging: 1) complexity in dealing with the overwhelming amount of data information from an advanced high resolution hyperspectral imaging sensor; 2) difficulty in leveraging spectral and spatial information across the sensed wavelengths; 3) struggles...
Images are usually represented by different groups of features, such as color, shape and texture attributes. In this paper, we propose a classification approach that integrates multiple features, such as spectral and spatial information. We refer this approach to multiple feature learning via rotation (MFL-R) strategy, which adopt a rotation-based ensemble method by using a data transformation approach...
The Karhunen-Loeve Transform (KLT) is a popular transform used in multiple image processing scenarios. Sometimes, the application of the KLT is not carried out as a single transform over an entire image. Rather, the image is divided into smaller spatial regions (segments), each of which is transformed by a smaller dimensional KLT. Such a situation may penalize the transform efficiency. An improvement...
Remotely extracting information about the biochemical properties of the materials in an environment from airborne- or satellite-based hyperspectral sensor has a variety of applications in forestry, agriculture, mining, environmental monitoring and space exploration. In this paper, we propose a new non-stationary covariance function, called exponential spectral angle mapper (ESAM) for predicting the...
We consider the total variation (TV) minimization problem used for compressive sensing and solve it using the generalized alternating projection (GAP) algorithm. Extensive results demonstrate the high performance of proposed algorithm on compressive sensing, including two dimensional images, hyperspectral images and videos. We further derive the Alternating Direction Method of Multipliers (ADMM) framework...
In this paper, a non-local based sparse representation (called as the NLSR) is proposed for the super-resolution of hyperspectral image. Specifically, the NLSR firstly uses the non-local Kmeans to partition pixels of low spatial resolution hyperspectral image into several classes. The non-local Kmeans can exploit the similar patterns and structures of the low spatial resolution image to enhance the...
Recently, low-rank representation (LRR) based methods have been used for hyperspectral image (HSI) denoising, which can simultaneously remove different types of noise: Gaussian noise, impulse noise, dead lines, and so on. However, the LRR based method does not make full use of the spatial information in HSI. In this paper, we integrate the superpixel segmentation (SS) into the LRR, and propose a novel...
A technique to describe the spatial / spectral features of hyperspectral images is introduced. These descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations, so called spatial invariances. Moreover, we also consider spectral invariances which are related to the composition of the pixels. Our approach is based...
Recently an algorithm to separate fluorescent and reflective components from a hyperspectral image has been reported, in which the important task of spectral unmixing of multiple fluorescent components was left unresolved. In this paper, we present the algorithm to simultaneously separate those components and unmix fluorophores (SSUF: Simultaneous Separation and Unmixing of Fluorescent components)...
This paper proposes a novel linear hyperspectral unmixing method based on 𝑙1−𝑙2 sparsity and total variation (TV) regularization. First, the enhanced sparsity based on 𝑙1−𝑙2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in sparse regression unmixing model. By taking the correlation between hyperspectral pixels into account, total variation is minimized...
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