DocumentCode :
2717178
Title :
Comparative Study of Relative Radiometric Normalization Techniques for Resourcesat1 LISS III Sensor Images
Author :
Pudale, Sujata R. ; Bhosle, Udhav V.
Author_Institution :
Pillai´´s Inst. of Inf. Technol., Mumbai
Volume :
3
fYear :
2007
fDate :
13-15 Dec. 2007
Firstpage :
233
Lastpage :
239
Abstract :
Land cover changes are producing profound impacts on global biodiversity, terrestrial carbon stocks, soil fertility and erosion. Manually detecting land cover changes using satellite images is a big hassle. Automation of change detection process overcomes the difficulty of manual detection. Automatic change detection methods require the images obtained at different times by satellite, are comparable in terms of radiometric characteristics. Relative radiometric normalization (RRN) process is used to prepare multitemporal image data sets for the detection of spectral changes associated with phenomena such as land cover change. A variety of image normalization methods, such as haze correction (HC), minimum-maximum (MM), mean-standard deviation (MS), pseudo-invariant features (PIF), dark and bright set (DB), simple regression (SR), and no-change (NC) set determined from scattergrams, are introduced which have been tested either with Landsat TM data, MSS data or both. In this paper, existing methods are tested to adopt for normalizing currently available high-resolution multispectral satellite images on different dates from Resourcesat1 LISS III sensor, which gives drastic change in spatial resolution and difference of available multispectral bands. Some improvements are introduced to get better results. The normalized results are compared in terms of visual inspection and statistical analysis.
Keywords :
artificial satellites; erosion; geophysical signal processing; image resolution; inspection; radiometry; remote sensing; soil; statistical analysis; Landsat TM data; MSS data; Resourcesat1 LISS III sensor images; change detection process; dark and bright set method; erosion; global biodiversity; haze correction method; high-resolution multispectral satellite images; land cover changes; mean-standard deviation method; minimum-maximum method; multitemporal image data sets; no-change set method; pseudo-invariant features method; relative radiometric normalization; scattergrams; simple regression method; soil fertility; statistical analysis; terrestrial carbon stocks; visual inspection; Automation; Biodiversity; Biosensors; Image sensors; Radiometry; Satellite broadcasting; Scattering; Soil; Strontium; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on
Conference_Location :
Sivakasi, Tamil Nadu
Print_ISBN :
0-7695-3050-8
Type :
conf
DOI :
10.1109/ICCIMA.2007.158
Filename :
4426373
Link To Document :
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