期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2012
卷号：XXXIX - B8
页码：289-294
DOI：10.5194/isprsarchives-XXXIX-B8-289-2012
出版社：Copernicus Publications
摘要：Evaluation of fuzzy based classifier to identify and map a specific crop using multi-spectral and time series data spanning over one growing season. The temporal data is pre-processed with respect to geo-registration and five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil- Adjusted Vegetation Index) and TVI (Triangular Vegetation Index). The noise classifier (NC) is evaluated in sub pixel classification approach and accuracy assessment has been carried out using fuzzy error matrix (FERM). The classification results with respect to the additional indices were compared in terms of image to image maximum classification accuracy. The overall accuracy observed in dataset 2 was 96.03% for TNDVI indices, using NC. Data used for this study was AWIFS for soft classification and LISS-III data for soft testing generated from Resourcesat-1(IRS-P6) satellite. The research indicates that appropriately used indices can incorporate temporal variations while extracting specific crop of interest with soft computing techniques for images having coarser spatial and temporal resolution remote sensing data
