Optimization of statistical learning algorithm for crop discrimination using remote sensing data

Agriculture is backbone of Indian economy, where agricultural production is estimated based upon its sown area. The probable reasons for not having accurate and transparent statistics on Indian agronomy would be the existing inadequate facilities, unstable mechanism, and sluggish government functionaries. With the advent of remote sensing technologies, researchers are optimistic towards addressing such problems. It would be great challenge to classify multi-spectral satellite images due to its complexity, processing skills, and classification. The reviews on problems and prospects of supervised and unsupervised classification techniques, is highlighted in this paper. Literatures stated that one of such statistical learning model is support vector machine algorithm, which reveals to be the best suitable algorithm for vegetation discrimination using remote sensing images. In this paper, an attempt is made in order to enhance the performance of SVM by optimizing its training part. The approach aims at investigating improvement corners on SVM classification for estimating agricultural area using remote sensing data and explores futuristic research in this domain too. Several attempts have been made using supervised SVM model, but use of EA for enhancement of SVM is the novelty that distinguishes this research weigh against the traditional approaches. Findings of such intermingling is put forth through this approach in constructive way so as to address crop identification for reducing the manual efforts taken to measure the agricultural area covered by the specific crops.