Classification of Field-Level Crop Types with a Time Series Satellite Data Using Deep Neural Network

Abstract

Crop-type classification has been relied upon on only spectral/spatial features. It does not provide the in-season information for researchers and decision makers for both practical and scientific purposes. While satellite images have desirable spectral and spatial information for classification, the ability to extract temporal information in satellite data remains a challenge due to revisiting frequency and gaps in the time period of capturing the data. To circumvent this challenge and generate more accurate results for an in-season crop-type classification, we have used Rectified Linear Unit (RLU) approach based on the concept of deep neural networks for intelligent and scalable computation of the classification process. The work was carried out on Nanjangud Taluk located in Mysuru District, Karnataka state on a Landsat data (multi-temporal scene) from 2010 to 2015. The results indicate that RLU shows an improvement of 5% to 15% for overall classification accuracy at 3 classes over the traditional against support vector machine. In comparison with KSRSC data set, this study reveals an accuracy of 85% for classifying rice and banana with an improvement of 10% over KSRCS crop-filed data.