Abstract
Abstract. 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.
Highlights
Time series of acquired multispectral image represent characteristics of a landscape and each element represented has a particular spectral response, which allows the researcher to get highly relevant information to make decisions without going to the field
The crop under consideration in this work is cotton cultivated in Aurangabad district of Maharashtra province in India
Data used for this study was AWiFS for soft classification and LISS-III data for soft testing from Resourcesat-1 (IRS-P6) satellite
Summary
Time series of acquired multispectral image represent characteristics of a landscape and each element represented has a particular spectral response, which allows the researcher to get highly relevant information to make decisions without going to the field. The time series of such vegetation indices observed over a period can help in further classification of the vegetation as crop and other type of vegetation. Robust statistics can be related to the concept of membership functions in fuzzy set theory or possibility distributions in possibility theory. This might explain the claim made by the proponents of fuzzy set theory that a fuzzy approach is more tolerant to variations and noise in the input data when compared with a crisp approach
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