TY - GEN
T1 - Collaborative classification of hyperspectral and LIDAR data using unsupervised image-to-image CNN
AU - Zhang, Mengmeng
AU - Li, Wei
AU - Wei, Xueling
AU - Li, Xiang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - Currently, how to efficiently exploit useful information from multi-source remote sensing data for better Earth observation becomes an interesting but challenging problem. In this paper, we propose an collaborative classification framework for hyperspectral image (HSI) and Light Detection and Ranging (LIDAR) data via image-to-image convolutional neural network (CNN). There is an image-to-image mapping, learning a representation from input source (i.e., HSI) to output source (i.e., LIDAR). Then, the extracted features are expected to own characteristics of both HSI and LIDAR data, and the collaborative classification is implemented by integrating hidden layers of the deep CNN. Experimental results on two real remote sensing data sets demonstrate the effectiveness of the proposed framework.
AB - Currently, how to efficiently exploit useful information from multi-source remote sensing data for better Earth observation becomes an interesting but challenging problem. In this paper, we propose an collaborative classification framework for hyperspectral image (HSI) and Light Detection and Ranging (LIDAR) data via image-to-image convolutional neural network (CNN). There is an image-to-image mapping, learning a representation from input source (i.e., HSI) to output source (i.e., LIDAR). Then, the extracted features are expected to own characteristics of both HSI and LIDAR data, and the collaborative classification is implemented by integrating hidden layers of the deep CNN. Experimental results on two real remote sensing data sets demonstrate the effectiveness of the proposed framework.
KW - Convolutional Neural Network
KW - Data Fusion
KW - Deep Learning
KW - Hyperspectral Image
UR - https://www.scopus.com/pages/publications/85056496704
U2 - 10.1109/PRRS.2018.8486164
DO - 10.1109/PRRS.2018.8486164
M3 - Conference contribution
AN - SCOPUS:85056496704
T3 - 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018
BT - 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018
Y2 - 19 August 2018 through 20 August 2018
ER -