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

Autor:	Bulent Ayhan, Antonio Plaza, David Gribben, Massimo Selva, Sergio Bernabe, Chiman Kwan
Rok vydání:	2020
Předmět:	Informática data fusion LiDAR 010504 meteorology & atmospheric sciences Computer science Science Near-infrared spectroscopy 0211 other engineering and technologies land cover classification hyperspectral EMAP synthetic bands Hyperspectral imaging 02 engineering and technology Land cover Sensor fusion 01 natural sciences Lidar General Earth and Planetary Sciences RGB color model 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing
Zdroj:	Remote Sensing; Volume 12; Issue 9; Pages: 1392 Remote sensing (Basel) 12 (2020). doi:10.3390/rs12091392 info:cnr-pdr/source/autori:Kwan, Chiman; Gribben, David; Ayhan, Bulent; Bernabe, Sergio; Plaza, Antonio; Selva, Massimo/titolo:Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data/doi:10.3390%2Frs12091392/rivista:Remote sensing (Basel)/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:12 E-Prints Complutense. Archivo Institucional de la UCM instname Remote Sensing, Vol 12, Iss 1392, p 1392 (2020)
ISSN:	2072-4292
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1582332710d8ed3f18557058c9e0dc93 https://doi.org/10.3390/rs12091392 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.