Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis

Padma, S.; Sanjeevi, S.

doi:10.1016/j.jag.2014.04.001

Cited by 77 publications

(47 citation statements)

References 19 publications

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“…All remaining spectra are assessed one by one using a spectral separability measure: only if a signature is sufficiently dissimilar from the already selected spectra, it will be included in the final selection. We selected a metric combining Jeffries Matusita distance and Spectral Angle (JMSA), which has been shown to perform better than each of these individual measures and can be used with varying spectral resolutions [36]. The calculation of JMSA between two spectra is performed on the non-normalized version of the spectra to include brightness differences in the similarity assessment.…”

Section: Proposed Algorithm: Amusesmentioning

confidence: 99%

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

et al. 2017

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Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of image-specific libraries prior to mapping. The size and heterogeneity of such a generic library requires an efficient pruning technique to extract site-specific spectral libraries. We propose the "Automated MUsic and spectral Separability based Endmember Selection technique" (AMUSES), which selects endmember subsets with respect to the image to be processed, while accounting for internal redundancy. Experiments on simulated hyperspectral data from Brussels (Belgium) showed that AMUSES selects more relevant endmembers compared to the conventional Iterative Endmember Selection (IES) approach. This ultimately improved mapping results (kappa increased from 0.71 to 0.83). Experiments on real HyMap data from Berlin (Germany) using a combination of libraries from different cities underlined the potential of AMUSES for handling libraries with increasing levels of generality (RMSE decreased from 0.18 to 0.15, while only using 55% of the number of spectra compared to IES). Our findings contribute to the value of generic spectral databases in the development of efficient urban mapping workflows.

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Section: Proposed Algorithm: Amusesmentioning

confidence: 99%

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

et al. 2017

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“…First of all, the Matusita distance function [29] is adopted to describe the relationship among N evidences.…”

Section: Evidence Correction Based On Reliability Index Andmentioning

confidence: 99%

“…Firstly, via adopting the Matusita distance function [29] and closeness degree function, the evidence reliability index and evidence consistency index are, respectively, addressed. Through the correction of potentially conflicting evidences based on the reliability index and consistency index, the conflicts caused by unreliable sensor sources are solved.…”

Section: Introductionmentioning

confidence: 99%

A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

Chen

Tian

2018

Journal of Sensors

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To eliminate potential evidence conflicts, an effective and accurate DS combination method is addressed in this paper. DS evidence theory is an outstanding information fusion approach with valid uncertainty treatment. Nevertheless, there are some limitations of the usage of the DS evidence theory. On the one hand, due to the complexity of a combat measurement environment and the inconsistency of sensor capabilities, sensor sources have enormous uncertainty, which would inevitably cause conflicts for evidence combination. On the other hand, DS combination rule realizes the unity property of fusing results with a compulsive normalization, which unavoidably leads to conflicting situations. To solve the possible evidence conflicts in a multisensor fusion system, we raise a robust DS combination method based on evidence correction and conflict redistribution. Firstly, two corrected indexes-the reliability index and consistency index-are separately addressed with the introduction of the Matusita distance function and closeness degree function. After the evidence modification based on two correction indexes, the conflicts caused by unreliable sensor sources are solved. Then, based on the corrected evidences, we put forward a weighted assignment of conflicting mass where the weight index lies on the evidence credibility. As the normalization step is abolished, the conflict redistribution strategy avoids the conflicts caused by straightforward normalization. Through comprehensive conflict management, the proposed DS combination method can not only guarantee the rationality and availability of fusing results, but also enhance the reliability and robustness of a multisensor system. Finally, three combination experiments with different conflicting degrees illustrate the advantage and superiority of the novel combination method for conflict management. Consequently, the innovation of the novel algorithm is verified.

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“…This matching algorithm defines the manner in which unknown or target spectra are compared with the known reference [15]. Here, the characteristics of known reference are called identification templates.…”

Section: Matching Of Spectral Shapementioning

confidence: 99%

A Spectral Signature Shape-Based Algorithm for Landsat Image Classification

Chen

Wang

et al. 2016

IJGI

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Land-cover datasets are crucial for earth system modeling and human-nature interaction research at local, regional and global scales. They can be obtained from remotely sensed data using image classification methods. However, in processes of image classification, spectral values have received considerable attention for most classification methods, while the spectral curve shape has seldom been used because it is difficult to be quantified. This study presents a classification method based on the observation that the spectral curve is composed of segments and certain extreme values. The presented classification method quantifies the spectral curve shape and takes full use of the spectral shape differences among land covers to classify remotely sensed images. Using this method, classification maps from TM (Thematic mapper) data were obtained with an overall accuracy of 0.834 and 0.854 for two respective test areas. The approach presented in this paper, which differs from previous image classification methods that were mostly concerned with spectral "value" similarity characteristics, emphasizes the "shape" similarity characteristics of the spectral curve. Moreover, this study will be helpful for classification research on hyperspectral and multi-temporal images.

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Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis

Cited by 77 publications

References 19 publications

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

A Spectral Signature Shape-Based Algorithm for Landsat Image Classification

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