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Traditional methods effectively used for processing multispectral data is usually limited to deal with hyperspectral images, which are characterized by massive data and higher spectral resolution. In this paper, multifractal is introduced to mining hyperspectral data by analyzing the holistic feature of spectral curve, and blanket method is adopted to compute fractal dimension (fractal signature)...
Virtual Dimensionality (VD) estimation is a key problem in feature/band selection and spectral mixture analysis of hyperspectral images. In this paper, a Double Subspace Projection (DSubP) based VD estimation algorithm is proposed. The pixel representation and image representation of a hyperspectral image are utilized to generate two subspaces according to the principal component analysis (PCA), respectively...
Hyper spectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have explored the application of morphological operations to integrate both spatial and spectral responses in hyper spectral data analysis. These operations rely on ordering pixel vectors in...
For the large-scale acquisition of hyperspectral or multispectral images, data distribution challenges the capabilities of available transmission technologies. It is therefore common to include data compression as part of a distribution system for remotely sensed imagery. While individual images can be compressed for transmission by taking into account the inherent spatial and spectral redundancy,...
In this paper, we used radiance-reflectance transformation method based on data character to get the reflectance of oil seawater since there is no atmosphere parameters measured. Furthermore, The characteristic spectrum of normal seawater and oil seawater are also analyzed and discussed, from which we drawn conclusions that the method can be used for obtaining reflectance data and aerial hyperspectral...
A multi-view based active learning method (AMDWVE) is proposed as a means to optimally construct the training set for supervised classification of hyperspectral data, thereby reducing the effort required to acquire ground reference data. The method explores the intrinsic multi-view information embedded in hyperspectral data. By adaptively and quantitatively measuring the disagreement level of different...
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. This paper introduces a new hierarchical structure representation for such images using binary partition trees (BPT). Based on region merging techniques using statistical measures, this region-based representation reduces the number of elementary primitives and...
Endmember extraction is an important technique in the context of spectral unmixing of remotely sensed hyperspectral data. Winter's N-FINDR algorithm is one of the most widely used and successfully applied methods for endmember extraction from remotely sensed hyperspectral images. Depending on the dimensionality of the hyperspectral data, the algorithm can be time consuming. In this paper, we propose...
A decision fusion approach is proposed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of supervised classification in class separation and the capability of unsupervised classification in reducing spectral variation impact in homogeneous regions. This approach simply adopts the majority voting rule, but can achieve the same objective...
This paper proposes the concept of using cellular automata (CAs) and adapted edge detection algorithms for edge detection in hyperspectral images. The approach consists of an edge detection CA and a post-processing CA (that implements morphological operations for denoising the edges). The CA approach is generalized in that it operates on any three-dimensional data cube, allowing for hyperspectral...
Feature extraction for the dimensionality reduction of hyperspectral data is performed by means of Auto-Associative Neural Networks. The algorithm performance is compared to the Principal Component Analysis and the Maximum Noise Fraction ones. Results of land cover pixel-based maps yielded by the reduced vector and a dedicated neural network classification algorithm are also reported.
In this paper, we explore the influence of band selection and dimensionality reduction of hyperspectral data on three point target detection algorithms. We wish to reduce the computational burden and to maximize the algorithms' performance by taking into consideration high spectral correlation. In order to measure the discrimination capability of target detection algorithms, we implemented a metric...
Automatic detection of sub-pixel materials presents a challenging problem in hyperspectral signal processing. While a number of different approaches have been proposed in the literature, few of these can be considered fully self-contained. In most cases, research has focused on only one area of sub-pixel detection such as endmember extraction, parameter estimation or detector architecture. This paper...
In this study, a novel spatial information based support vector machine for hyperspectral image classification, named spatial-contextual semi-supervised support vector machine (SC3SVM), is proposed. This approach modifies the SVM algorithm by using the spectral information and spatial-contextual information. The concept of SC3SVM is to utilize other information, obtain from the pixels of a neighborhood...
Virtual dimensionality (VD) was originally developed for estimating the number of spectrally distinct signatures present in hyperspectral data. The effectiveness of the VD is determined by the technique used for VD estimation. This paper develops an orthogonal subspace projection (OSP) technique to estimate the VD. The idea is derived from linear spectral mixture analysis. A similar idea was also...
The next generation of Earth-observing spacecraft are likely to generate enormous volumes of data. A major challenge lies in the conversion of these mountains of data into information useful to researchers and other users. Hierarchical segmentation is one way to detect relationships among regions in a hyperspectral image. We implemented this algorithm on a next-generation space-capable hardware platform,...
A new multiple classifier method for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers...
Hyperspectral unmixing can be considered as a blind source separation (BSS) and/or independent component analysis (ICA) problem. This paper presents a new noise-resistant subband decomposition BSS/ICA approach for hyperspectral unmixing. Subband decomposition BSS relaxes the assumption that the source signals are mutual independent, which has been proved successful in some BSS applications. However,...
Accurate and fast data unmixing is key to most applications employing hyperspectral data. Among the large number unmixing approaches, Blind Source Separation (BSS) has been employed successfully through a variety of techniques, yet most of these approaches continue to be computationally expensive due to their iterative nature. In this context, it is imperative to seek efficient approaches that leverage...
Traditional Nonnegative Matrix Factorization (NMF) algorithm is sensitive to the initial value when being applied to hyperspectral unmixing, because of the local minima in the objective function. In order to solve the problem, two constraints of abundance separation and smoothness are introduced into the NMF algorithm. The proposed algorithm retains the advantages of NMF, and effectively overcomes...
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