Hyperspectral images adaptive dimensionality reduction optimized by<italic style="font-style: italic"> </italic>JM transform

Xiaoyan KANG; Aiwu ZHANG; Shaoxing HU; Qing XIAO; Shatuo CHAI

doi:10.11834/jrs.20208178

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Hyperspectral images adaptive dimensionality reduction optimized by JM transform
Vol. 24, Issue 1, Pages: 67-75(2020)
Received：10 April 2018，

Published：07 January 2020
DOI： 10.11834/jrs.20208178
稿件说明：

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康孝岩,张爱武,胡少兴,肖青,柴沙驼.2020.高光谱图像的JM变换自适应降维.遥感学报,24(1): 67-75 DOI： 10.11834/jrs.20208178.

KANG Xiaoyan,ZHANG Aiwu,HU Shaoxing,XIAO Qing,CHAI Shatuo. 2020. Hyperspectral images adaptive dimensionality reduction optimized byJM transform. Journal of Remote Sensing(Chinese). 24(1): 67-75 DOI： 10.11834/jrs.20208178.

摘要

在无需先验标签样本的情况下，非监督降维可以有效简化高光谱图像的特征空间，避免目标分类中的霍夫效应。本文提出JM非线性变换优化的自适应降维模型来研究面向图像目标分类的高光谱波段选择问题。该方法考虑波段的信息量和独立性等两个重要因子，针对其测度方法的差异性问题，引入JM变换函数进行规范化优化。选用线阵高光谱和面阵显微光谱等两个图像数据集，在

最邻近和随机森林分类器下，进行了多组监督分类实验，结果表明，在Kappa系数、总体分类精度和平均分类精度上，本文方法均优于3种非监督方法MABS、InfFS和LSFS。说明本文提出的JM变换的自适应降维模型能够有效降低特征维度，满足高光谱图像分类的高精度要求。

Abstract

Hyperspectral remote sensing images

which collect rich spectral and spatial information of observed targets

usually contain dozens to hundreds of narrow bands with wavelengths ranging from the visible light region to the near-infrared spectra. With such an abundant number of spectral features

hyperspectral images (HSI) allow us to distinguish different of objects or targets by rule and line. Unfortunately

such high-dimensionality data pose a challenge in data transmission

storage

and processing. Specifically

those HSIs with high redundancy information and strong correlation are prone to a Hughes phenomenon during the image classification process. Therefore

dimensionality reduction is necessary for targets classification. Moreover

without using prior label samples

unsupervised dimensionality reduction can effectively simplify the HSI feature space

and prevent the Hughes phenomenon in the targets classification.

In this paper

the Jeffries–Matusita (JM) modified adaptive band selection (JM

ABS) method is proposed to extract proper features from HSI datasets. Generally speaking

a band that contains many information and demonstrates strong independence is a very important feature that helps unsupervised band selection methods to classify targets. The JM

ABS method considers both the information capacity and independence of HSI bands. Given the significant differences in the measurements of a band’s information capacity and its independence

we introduce the JM transform function to normalize the distributions of the information capacity and the independence of HSI data. Thus JM

ABS shows that both the information capacity and the independence are equally important in unsupervised dimensionality reduction.

We also compare our proposed JM

ABS method against three typical methods

namely

the modified adaptive band selection method

the Laplacian score feature selection method

and the infinite feature selection method. By using random training samples

we perform supervised classification experiments on two kinds of HSI public datasets (linear and planar arrays). The results demonstrate that JM

ABS outperforms the other three typical methods in terms of Kappa value

overall classification accuracy

and average classification accuracy. Moreover

under a small number of bands

ABS can reach a high and stable level regardless of the different datasets and different classifiers used.

The proposed JM

ABS can select the proper features of HSI datasets for their classification application. The JM transform function is a kind of nonlinear distribution function that can standardize variables from different scales to the same. To demonstrate the feasibility of JM transform optimization

we set the same weight for the information content and the independence. In our future work

we will explore the similarities and differences between the information capacity and the independence in dimensionality reduction.

关键词

Keywords

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