RT Journal Article
T1 Deep Learning for Land Cover Classification Using Only a Few Bands
A1 Kwan, Chiman
A1 Ayhan, Bulent
A1 Budavari, Bence
A1 Lu, Yan
A1 Pérez, Daniel
A1 Li, Jiang
A1 Bernabé García, Sergio
A1 Plaza, Antonio
AB There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.
PB MDPI
SN 2072-4292
YR 2020
FD 2020-06-22
LK https://hdl.handle.net/20.500.14352/8355
UL https://hdl.handle.net/20.500.14352/8355
LA eng
NO US Department of Energy
DS Docta Complutense
RD 7 abr 2025