Artificial neural networks in the improvement of spatial resolution of thermal infrared data for improved landuse classification

Author

Venkateshwarlu, Ch. ; Gopal Rao, K. ; Prakash, Aravind

fYear

2003

fDate

22-23 May 2003

Firstpage

162

Lastpage

166

Abstract

The spatial resolution of remotely sensed (RS) data in the thermal infrared (TIR) range is very coarse compared to the very fine resolutions in the visible (VIS) and near infrared (NIR) ranges. Despite, the information on emissive properties of TIR data that is complementary to the reflective properties of the VIS and NIR data, the application of TIR data has been rather restricted, mainly due to its coarse spatial resolution. Artificial neural networks (ANN) have proved to be far superior [Govindaraju, R. S. and Rao, A. R., 2000][Heermann, P. D. and Khazenei, K., 1992] to the statistical methods in many applications. Studies have been carried out on the applicability of ANN in the improvement of effective spatial resolution of Landsat-5, TM band 6 (TIR) daytime and nighttime data. The present paper reports the methodology developed and the results of the studies. The results are compared with those of a statistical approach.

Keywords

geophysics computing; image classification; image resolution; infrared imaging; neural nets; statistical analysis; terrain mapping; Landsat-5; TM band 6; artificial neural networks; improved landuse classification; near infrared range; remotely sensed data; spatial resolution improvement; statistical methods; thermal infrared data; thermal infrared range; visible range;

fLanguage

English

Publisher

ieee

Conference_Titel

Remote Sensing and Data Fusion over Urban Areas, 2003. 2nd GRSS/ISPRS Joint Workshop on

Conference_Location

Berlin, Germany

Print_ISBN

0-7803-7719-2

Type

conf

DOI

10.1109/DFUA.2003.1219979

Filename

5731021