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

Kwan, Chiman; Gribben, David; Ayhan, Bulent; Bernabe, Sergio; Plaza, Antonio; Selva, Massimo

doi:10.3390/rs12091392

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

Journal Article · Tue Apr 28 00:00:00 EDT 2020 · Remote Sensing

DOI:https://doi.org/10.3390/rs12091392· OSTI ID:1668674

^[1]; Gribben, David ^[2]; Ayhan, Bulent ^[2]; Bernabe, Sergio ^[3]; Plaza, Antonio ^[4]; ^[5]

Applied Research LLC, Rockville, MD (United States); Applied Research LLC
Applied Research LLC, Rockville, MD (United States)
Univ. Complutense Madrid (Spain). Dept. of Computer Architecture and Automation
Univ. of Extramadura, Caceres (Spain)
National Research Council (CNR), Florence (Italy). Institute of Applied Physics "Nello Carrara" (IFAC)

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.

View Accepted Manuscript (DOE)

Research Organization:: Applied Research LLC, Rockville, MD (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0019936

OSTI ID:: 1668674

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 9 Vol. 12; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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