Abstract
Abstract. Agriculture holds a pivotal role in context to India, which is basically agrarian economy. Crop type identification is a key issue for monitoring agriculture and is the basis for crop acreage and yield estimation. However, it is very challenging to identify a specific crop using single date imagery. Hence, it is highly important to go for multi-temporal analysis approach for specific crop identification. This research work deals with implementation of fuzzy classifier; Possibilistic c-Means (PCM) with and without kernel based approach, using temporal data of Landsat 8- OLI (Operational Land Imager) for identification of wheat in Radaur City, Haryana. The multi- temporal dataset covers complete phenological cycle that is from seedling to ripening of wheat crop growth. The experimental results show that inclusion of Gaussian kernel, with Euclidean Norm (ED Norm) in Possibilistic c-Means (KPCM), soft classifier has been more robust in identification of the wheat crop. Also, identification of all the wheat fields is dependent upon appropriate selection of the temporal date. The best combination of temporal data corresponds to tillering, stem extension, heading and ripening stages of wheat crop. Entropy at testing sites of wheat has been used to validate the classified results. The entropy value at testing sites was observed to be low, implying lower uncertainty of existence of any other class at wheat test sites and high certainty of existence of wheat crop.
Highlights
Classification of the remote sensing imagery has always drawn the attention of researchers
In order to achieve the research objectives, mapping of wheat crop was carried out using fuzzy classification approach with and without kernel based approach on multi-temporal data in Radaur City, Haryana
From the results of the carried out research, it is evident that multi-temporal images are competent to identify specific crop; wheat
Summary
Classification of the remote sensing imagery has always drawn the attention of researchers. The technique generates fractional estimation of classes in a pixel and is a better option to generate classified maps with better accuracy using remote sensing datasets. FCM and PCM fail to give results with higher accuracy, when the classes are linearly non- separable In such a situation, inclusion of kernels in the existing algorithms is the solution. Kumar et al, 2006, studied the effect of different kernels while generating density estimation using SVM with respect to overall sub-pixel classification accuracy of multi-spectral data. Fuzzy based noise classifier (NC) was used on time series of five vegetation indices (SR, NDVI, TNDVI, SAVI and TVI) derived from AWiFS for cotton crop identification. In order to achieve the research objectives, mapping of wheat crop was carried out using fuzzy classification approach with and without kernel based approach on multi-temporal data in Radaur City, Haryana.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
