TY - JOUR
T1 - Classification of hyperspectral and LIDAR data using extinction profiles with feature fusion
AU - Zhang, Mengmeng
AU - Ghamisi, Pedram
AU - Li, Wei
N1 - Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/10/3
Y1 - 2017/10/3
N2 - Hyperspectral images comprise hundreds of narrow contiguous wavelength bands which include wealth spectral information, and a great potential of Light detection and ranging (LIDAR) data lies in its benefits of height measurements, which can be used as complementary information for the classification of hyperspectral data. In this paper, a feature-fusion strategy of hyperspectral and LIDAR data is taken into account in order to develop a new classification framework for the accurate analysis of a surveyed area. The proposed approach employs extinction profiles (EPs) extracted with extinction filters computed on both hyperspectral and LIDAR images, leading to a fusion of the spectral, spatial, and elevation features. Experimental results obtained by using a real hyperspectral image along with LIDAR-derived digital surface model (DSM) collected over the University of Houston campus and its neighboring urban area demonstrate the effectiveness of the proposed framework.
AB - Hyperspectral images comprise hundreds of narrow contiguous wavelength bands which include wealth spectral information, and a great potential of Light detection and ranging (LIDAR) data lies in its benefits of height measurements, which can be used as complementary information for the classification of hyperspectral data. In this paper, a feature-fusion strategy of hyperspectral and LIDAR data is taken into account in order to develop a new classification framework for the accurate analysis of a surveyed area. The proposed approach employs extinction profiles (EPs) extracted with extinction filters computed on both hyperspectral and LIDAR images, leading to a fusion of the spectral, spatial, and elevation features. Experimental results obtained by using a real hyperspectral image along with LIDAR-derived digital surface model (DSM) collected over the University of Houston campus and its neighboring urban area demonstrate the effectiveness of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85020532300&partnerID=8YFLogxK
U2 - 10.1080/2150704X.2017.1335902
DO - 10.1080/2150704X.2017.1335902
M3 - Article
AN - SCOPUS:85020532300
SN - 2150-704X
VL - 8
SP - 957
EP - 966
JO - Remote Sensing Letters
JF - Remote Sensing Letters
IS - 10
ER -