Journal ArticleDOI
Clustering-Based Hyperspectral Band Selection Using Information Measures
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TLDR
This paper presents a technique for dimensionality reduction to deal with hyperspectral images based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance.Abstract:
Hyperspectral imaging involves large amounts of information. This paper presents a technique for dimensionality reduction to deal with hyperspectral images. The proposed method is based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance. This aim is pursued using information measures, such as distances based on mutual information or Kullback-Leibler divergence, in order to reduce data redundancy and non useful information among image bands. Experimental results include a comparison among some relevant and recent methods for hyperspectral band selection using no labeled information, showing their performance with regard to pixel image classification tasks. The technique that is presented has a stable behavior for different image data sets and a noticeable accuracy, mainly when selecting small sets of bands.read more
Citations
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Book ChapterDOI
Statistical Pattern Recognition
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
Journal ArticleDOI
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
TL;DR: A new spectral-spatial classification scheme for hyperspectral images is proposed that improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification.
Journal ArticleDOI
Convolutional neural networks for hyperspectral image classification
Shiqi Yu,Sen Jia,Chunyan Xu +2 more
TL;DR: An efficient CNN architecture has been proposed to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final CNN outputs are the predicted class-related results.
Journal ArticleDOI
Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking
Qi Wang,Jianzhe Lin,Yuan Yuan +2 more
TL;DR: This paper proposes to eliminate the drawbacks of traditional salient band selection methods by manifold ranking and puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper.
Journal ArticleDOI
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox
Behnood Rasti,Danfeng Hong,Renlong Hang,Pedram Ghamisi,Xudong Kang,Jocelyn Chanussot,Jon Atli Benediktsson +6 more
TL;DR: In this article, the curse of dimensionality of hyperspectral images (HSIs) has been discussed, which is a challenge to conventional techniques for accurate analysis of HSIs.
References
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