Abstract

During the last decade, hyperspectral images have attracted increasing interest from researchers worldwide. They provide more detailed information about an observed area and allow an accurate target detection and precise discrimination of objects compared to classical RGB and multispectral images. Despite the great potentialities of hyperspectral technology, the analysis and exploitation of the large volume data remain a challenging task. The existence of irrelevant redundant and noisy images decreases the classification accuracy. As a result, dimensionality reduction is a mandatory step in order to select a minimal and effective images subset. In this paper, a new filter approach normalized mutual synergy (NMS) is proposed in order to detect relevant bands that are complementary in the class prediction better than the original hyperspectral cube data. The algorithm consists of two steps: images selection through normalized synergy information and pixel classification. The proposed approach measures the discriminative power of the selected bands based on a combination of their maximal normalized synergic information, minimum redundancy and maximal mutual information with the ground truth. A comparative study using the support vector machine (SVM) and k-nearest neighbor (KNN) classifiers is conducted to evaluate the proposed approach compared to the state of art band selection methods. Experimental results on three benchmark hyperspectral images proposed by the NASA "Aviris Indiana Pine", "Salinas" and "Pavia University" demonstrated the robustness, effectiveness and the discriminative power of the proposed approach over the literature approaches. Keywords: Hyperspectral images; target detection; pixel classification; dimensionality reduction; band selection; information theory; mutual information; normalized synergy

Highlights

  • In the decade, the exploitation of hyperspectral imaging [1] will experience a spectacular development thanks to the technological imaging evolution growing in many areas

  • In order to evaluate the performance of the proposed filter method, we will use three real hyperspectral datasets from NASA‟s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) [10] and the Reflective Optics System Imaging Spectrometer (ROSIS) [11]

  • 1) Classification results on HS image aviris indiana pine: Table I presents a comparison of classification results between the proposed approach (NMS) versus the information theorybased filters (MIBF, Joint Mutual Information (JMI), Double Input Symmetrical Relevance (DISR) and normalized mutual information algorithm (NMI))

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Summary

INTRODUCTION

The exploitation of hyperspectral imaging [1] will experience a spectacular development thanks to the technological imaging evolution growing in many areas. They evaluate the band‟s relevance based on the classification accuracy and generally reach promising results. These approaches are very expensive in terms of computational complexity and may suffer from over-fitting to the learning algorithm. They are based on the maximization of a certain evaluation function. This paper contributes to the knowledge in the area of hyperspectral dimensionality reduction by proposing a new approach based on normalized synergic correlation. This paper reviews the state of art band selection methods highlighting their common limitations and comparing their performance versus the proposed algorithm.

BACKGROUND
Limitation of State of Art Methods
EXPERIMENTAL RESULTS AND ANALYSIS
Classifiers and Evaluation Metrics
Classification Results and Discussion
CONCLUSION
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