An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery

被引:7
|
作者
Li, Haishan [1 ]
Wu, Ke [1 ]
Xu, Ying [2 ,3 ]
机构
[1] China Univ Geosci, Inst Geophys & Geomat, Wuhan 430074, Peoples R China
[2] Minist Nat Resources, Natl Satellite Ocean Applicat Serv, Beijing 100081, Peoples R China
[3] Minist Nat Resources, Key Lab Space Ocean Remote Sensing & Applicat, Beijing 100081, Peoples R China
关键词
spectral unmixing; hyperspectral image; change detection; multiple changes; joint matrix; convolutional neural network; UNSUPERVISED CHANGE DETECTION; MIXTURE ANALYSIS; COMPONENT ANALYSIS; FRAMEWORK; MODEL;
D O I
10.3390/rs14112523
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Hyperspectral remote sensing image (HSI) include rich spectral information that can be very beneficial for change detection (CD) technology. Due to the existence of many mixed pixels, pixel-wise approaches can lead to considerable errors in the resulting CD map. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of land cover. Subsequently, the CD map is created by comparing the abundance images. However, based only on the abundance images created through the SU method, they are unable to effectively provide detailed change information. Meanwhile, the features of change information cannot be sufficiently extracted by the traditional sub-pixel CD framework, which leads to a poor CD result. To address these problems, this paper presents an integrated CD method based on multi-endmember spectral unmixing, joint matrix and CNN (MSUJMC) for HSI. Three main steps are considered to accomplish this task. First, considering the endmember spectral variability, more reliable endmember abundance information is obtained by multi-endmember spectral unmixing (MSU). Second, the original image features are incorporated with the abundance images using a joint matrix (JM) algorithm to provide more temporal and spatial land cover change information characteristics. Third, to efficiently extract the change features and to better handle the fused multi-source information, the convolutional neural network (CNN) is introduced to realize a high-accuracy CD result. The proposed method has been verified on simulated and real multitemporal HSI datasets, which provide multiple changes. Experimental results verify the effectiveness of the proposed approach.
引用
收藏
页数:19
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