Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data

Remote Sensing

Bulent Ayhan¹  Chiman Kwan¹  David Gribben¹  Sergio Bernabe²  Antonio Plaza³  Massimo Selva⁴ 

[1] Applied Research LLC, Rockville, MD 20850, USA;Department of Computer Architecture and Automation, Complutense University of Madrid, 28040 Madrid, Spain;Department of Technology of Computers and Communications, University of Extremadura, 10003 Cáceres, Spain;Institute of Applied Physics “Nello Carrara”, IFAC- CNR, Research Area of Florence, 50019 Sesto Fiorentino (FI), Italy;

关键词: land cover classification;    hyperspectral;    EMAP;    synthetic bands;    LiDAR;    data fusion;   

DOI  :  10.3390/rs12091392

来源: DOAJ

【 摘 要 】

Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.

【 授权许可】

Unknown