TY - JOUR
T1 - GETNET
T2 - A General End-To-End 2-D CNN Framework for Hyperspectral Image Change Detection
AU - Wang, Qi
AU - Yuan, Zhenghang
AU - Du, Qian
AU - Li, Xuelong
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2019/1
Y1 - 2019/1
N2 - Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of the hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high-dimension problem and explore abundance information, this paper presents a general end-To-end 2-D convolutional neural network (CNN) framework for hyperspectral image CD (HSI-CD). The main contributions of this paper are threefold: 1) mixed-Affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multisource information; 2) 2-D CNN is designed to learn the discriminative features effectively from the multisource data at a higher level and enhance the generalization ability of the proposed CD algorithm; and 3) the new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate that the proposed method outperforms most of the state of the arts.
AB - Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of the hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high-dimension problem and explore abundance information, this paper presents a general end-To-end 2-D convolutional neural network (CNN) framework for hyperspectral image CD (HSI-CD). The main contributions of this paper are threefold: 1) mixed-Affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multisource information; 2) 2-D CNN is designed to learn the discriminative features effectively from the multisource data at a higher level and enhance the generalization ability of the proposed CD algorithm; and 3) the new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate that the proposed method outperforms most of the state of the arts.
KW - 2-D convolutional neural network (CNN)
KW - change detection (CD)
KW - deep learning
KW - hyperspectral image (HSI)
KW - mixed-Affinity matrix
KW - spectral unmixing
UR - http://www.scopus.com/inward/record.url?scp=85050503570&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2018.2849692
DO - 10.1109/TGRS.2018.2849692
M3 - 文章
AN - SCOPUS:85050503570
SN - 0196-2892
VL - 57
SP - 3
EP - 13
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 1
M1 - 8418840
ER -