DEEP CONVOLUTION NEURAL NETWORKS WITH RESNET ARCHITECTURE FOR SPECTRAL-SPATIAL CLASSIFICATION OF DRONE BORNE AND GROUND BASED HIGH RESOLUTION HYPERSPECTRAL IMAGERY

Abstract

Drones have been of vital importance in the fields of surveillance, mapping, and infrastructure inspection. Drones have played a vital role in acquiring high-resolution images and with the present need for precision farming, drones have helped in crop classification and monitoring various crop patterns. With the recent advancement in computational power and development of robust algorithms to carry out deep feature learning and neural network, based learning such techniques have regained prominence in contemporary research areas such as classification of common 2-D and 3-D images, object detection, etc. In our research, we propose a deep convolutional neural network architecture (CNN) for the classification of aerial images captured by drones and high-resolution Terrestrial Hyperspectral (THS or HSI) which includes 6-layers and with weights optimized along with the input layer, the convolutional layer, the max-pooling layer, the fully connected layer, softmax probability classifier, and the output layer. We have acquired THS (using Cubert-GmbH data) and drone agricultural data of seasonal crops sowed during the months of March-June for the year 2017. Crop patterns include Cabbage, Eggplant, and Tomato with varying nitrogen concentrations in the region of Bangalore, Southern India. To study the influence and impact of CNN, the ResNets model has been applied. ResNets model and architecture are combined with a deep learning network followed by a recurrent neural learning network model (RCNN). The HSI input layer with corresponding ground truth data for the region is fed into the ResNets model with a spectral and spatial residual network for the 7*7*139 input Hyperspectral Imagery (HSI) volume. The network includes two spectral and two spatial residual blocks. An average pooling layer and a fully connected layer transform into a 5*5*24 spectral-spatial feature volume further to a single output feature vector. At present we use an RMSProp optimizer for error loss minimization which when applied to the drone data was able to achieve an overall accuracy of 97.16%. Similarly, for cabbage, eggplant and tomato acquired through the same method we achieved overall accuracy at 87.619%, 89.25%, and 80.566% respectively in comparison to ground truth labels. Drones and ground-based datasets equipped with good computational techniques have become promising tools for improving the quality and efficiency of precision agriculture today.