Bands Sensitive Convolutional Network for Hyperspectral Image Classification (original) (raw)

2016

Abstract

Hyperspectral image (HSI) classification deals with the problem of pixel-wise spectrum labelling. Traditional HSI classification algorithms focus on two major stages: feature extraction and classifier design. Though studied for decades, HSI classification hasn't been perfectly solved. One of the main reasons relies on the fact that features extracted by embedding methods can hardly match an ad hoc classifier. Recently, deep learning methods achieve an end-to-end mechanism and can learn features suitable for classification from the raw data. Inspired by the newly proposed work on deep learning for HSI classification, in this paper, we propose to build a deep convolutional network based on the analysis of spectral band discriminative characteristics. More specifically, we first split the spectrum bands into groups based on their correlation relationships. Then we build a band variant CNN submodel, where each group is modelled by one of those submodels. Meanwhile, a conventional CN...

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