Remote Sensing Commons^™

Deep Learning For Land Cover Classification Using Only A Few Bands, Chiman Kwan, Bulent Ayhan, Bence Budavari, Yan Lu, Daniel Perez, Jiang Li, Sergio Bernabe, Antonio Plaza

Electrical & Computer Engineering Faculty Publications

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 …