摘要
高光谱图像波段选择需考虑波段信息。传统香农信息熵指标仅考虑图像的组分信息(像元的种类和比例),忽略了图像的空间配置信息(像元的空间分布),后者可由玻尔兹曼熵刻画。其中,Wasserstein配置熵删除了连续像元的冗余信息,但局限于四邻域,本文将Wasserstein配置熵拓展至八邻域。以印度松木试验场和意大利帕维亚大学高光谱图像为例,使用Wasserstein配置熵差异值测度波段相关性,通过非监督次优搜索法确定最优波段组合,并用支持向量机分类。比较基于Wasserstein配置熵差异值、互信息、4种标准化互信息和两种相对熵变体的图像分类精度。结果表明,四邻域和八邻域Wasserstein配置熵差异值均可用于高光谱图像波段选择,当选择少量波段时优势尤为明显,且八邻域整体优于四邻域。
Band selection relies on the quantification of band information.Conventional measurements such as Shannon entropy only consider the composition information(e.g.,types and ratios of pixels)but ignore the configuration information(e.g.,the spatial distribution of pixels).The latter could be quantified by Boltzmann entropy.Among all the metrics of Boltzmann entropy,the Wasserstein metric-based configuration entropy(Wasserstein entropy for short)removes the redundant information of the continuous pixels.However,it is limited to 4-neighborhood.This article improves it to 8-neighborhood.Taking the hyperspectral images of Indian Pines and Italian Pavia University as examples,we used the difference of Wasserstein entropy to measure band correlation and then employed the unsupervised sub-optimal searching algorithm to determine the optimal band combination.We used the support vector machine classifier for image classification.Finally,we compared the accuracy of image classification based on the difference of Wasserstein entropy,mutual information,four types of normalized mutual information,and two variants of relative entropy.Results show that both the 4-neighborhood and 8-neighborhood Wasserstein entropy can be used for band selection of hyperspectral images,especially when few bands are considered.The 8-neighborhood Wasserstein entropy works better than 4-neighborhood.
作者
张红
吴智伟
王继成
高培超
ZHANG Hong;WU Zhiwei;WANG Jicheng;GAO Peichao(Institute for Global Innovation and Development,East China Normal University,Shanghai 200062,China;School of Urban and Regional Science,East China Normal University,Shanghai 200241,China;Faculty of Geosciences & Environmental Engineering,Southwest Jiaotong University,Chengdu 611756,China;State Key Laboratory of Earth Surface Processes and Resource Ecology,Beijing Normal University,Beijing 100875,China)
出处
《测绘学报》
EI
CSCD
北大核心
2021年第3期405-415,共11页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(4191316)
四川省科技支撑计划(2020YJ0325)
成都市重点研发支撑计划(2019-YF05-02119-SN)
上海市哲学社会科学规划课题(2020BGL034)
地表过程与资源生态国家重点实验室开放基金(2020-KF-03)
中央高校基本科研业务费专项资金(2019NTST02)。
作者简介
第一作者:张红(1981—),女,博士,副教授,研究方向为地理信息分析、时空建模与复杂网络,E-mail:hzhang@re.ecnu.edu.cn;通信作者:高培超,E-mail:gaopc@bnu.edu.cn。
