Urban land-cover classification based on airborne hyperspectral data and field observation

Fumio Yamazaki; Konomi Hara; Wen Liu

doi:10.1117/12.2066616

21 October 2014 Urban land-cover classification based on airborne hyperspectral data and field observation

Fumio Yamazaki, Konomi Hara, Wen Liu

Proceedings Volume 9244, Image and Signal Processing for Remote Sensing XX; 92440P (2014) https://doi.org/10.1117/12.2066616
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

Using a dataset from the 2013 IEEE data fusion contest, a basic study to classify urban land-cover was carried out. The spectral reflectance characteristics of surface materials were investigated from the airborne hyperspectral (HS) data acquired by CASI-1500 imager over Houston, Texas, USA. The HS data include 144 spectral bands in the visible to near-infrared (380 nm to 1050 nm) regions. A multispectral (MS) image acquired by WorldView-2 satellite was also introduced in order to compare it with the HS image. A field measurement in the Houston’s test site was carried out using a handheld spectroradiometer by the present authors. The reflectance of surface materials obtained by the measurement was also compared with the pseudo-reflectance of the HS data and they showed good agreement. Finally a principal component analysis was conducted for the HS and MS data and the result was discussed.

Citation Download Citation

Fumio Yamazaki, Konomi Hara, and Wen Liu "Urban land-cover classification based on airborne hyperspectral data and field observation", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92440P (21 October 2014); https://doi.org/10.1117/12.2066616

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available