Convolutional Neural Network (CNN) for Crop - Classification of Drone Acquired Hyperspectral Imagery

Abhinav Galodha, Rahul Vashisht, Rama Rao Nidamanuri, Anandakumar M. Ramiya

Abstract: Hyperspectral remote sensing has gained prominence in the past two decades. The high resolution imagery of Unmanned Aerial Vehicle (UAV), Terrestrial Hyperspectral spectroradiometer (THS) have gained popularity. In this study, we develop a Convolutional Neural Network (CNN) based architecture to accurately classify UAV, THS dataset for the region of Bangalore, India. To carry out feature extraction, classification, prediction and analysis a CNN Conv-4 and CNN Conv-6 model is designed with varying kernel sizes thus, extracting multiple features. The results show a visual and quantitative measure of how CNN Conv-6 has better capability and higher accuracy. Also, a graphical plot of training, validation accuracy and loss as a function of number of iterations is generated as an output at the end of prediction. This is to clarify the threshold limit for number of epochs required to run the model for classification and prediction.