Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data

Abstract

The Resourcesat-2 is a highly suitable satellite for crop classification studies with its improved features and capabilities. Data from one of its sensors, the linear imaging and self-scanning (LISS IV), which has a spatial resolution of 5.8 m, was used to compare the relative accuracies achieved by support vector machine (SVM), artificial neural network (ANN), and spectral angle mapper (SAM) algorithms for the classification of various crops and non-crop covering a part of Varanasi district, Uttar Pradesh, India. The separability analysis was performed using a transformed divergence (TD) method between categories to assess the quality of training samples. The outcome of the present study indicates better performance of SVM and ANN algorithms in comparison to SAM for the classification using LISS IV sensor data. The overall accuracies obtained by SVM and ANN were 93.45% and 92.32%, respectively, whereas the lower accuracy of 74.99% was achieved using the SAM algorithm through error matrix analysis. Results derived from SVM, ANN, and SAM classification algorithms were validated with the ground truth information acquired by the field visit on the same day of satellite data acquisition.

Identification and Mapping of Marine Submerged Aquatic Vegetation in Shallow Coastal Waters with WorldView-2 Satellite Data

“…The highest overall accuracy found in SVM and ANN algorithms was 93.45% and 92.32%, respectively. Likewise, the SAM method has low accuracy among the classification methods (74.99%) (Kumar et al, 2015). In this study, MDiP and MiD methods demonstrated potential for identifying and mapping MSAV with WV2 multispectral high-spatial resolution (MHSR) satellite imagery.…”

Evaluating the effects of spatial resolution on land use and land cover classification accuracy

“…The availability of high resolution Indian remote sensing satellite data from Resourcesat-2 LISS IV is expected to have a considerable effect on LULC classification accuracy. In some studies LISS IV data have been used for classification of crops and other land cover features [6][7].…”

Artificial neural network with different learning parameters for crop classification using multispectral datasets

“…The ANN classifier, a more sophisticated and robust classifier of image classification has been employed in the classification applications [3], [6]- [9]. Some researchers have used the back propagation algorithm for crop classification and estimation of crop variables in Varanasi [10]- [11]. However, so many studies have reported some problems during use of back propagation ANN for crop classification and other land cover features [3], [12].…”