Evaluation of fuzzy-based classifiers for cotton crop identification

Abstract

In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%.

Keywords:

• possibilistic c-means (PCM)
• indices
• fuzzy error
• matrix (FERM)
• noise classifier (NC)
• spectral indices

Acknowledgements

The authors are thankful to learned reviewers for their valuable guidence and suggestions in improving the manuscript. The authors are also highly thankful to the editor of Geocarto International Journal for the suggestions in reformatting this manuscript.