CaneSat dataset to leverage convolutional neural networks for sugarcane classification from Sentinel-2

Abstract

The ubiquitous deep learning (DL) in remote sensing (RS) motivates the most challenging problem of crop classification. To perpetrate such an exigent task, an attempt is made to prepare a novel dataset, the CaneSat dataset, in two formats: RGB color space and geo-tiff images, covering the region of four talukas in Karnataka, India. This research aims to build a model for sugarcane classification using two-dimensional convolutional neural network (CNN or ConvNet) applying RS time series data. Further, the study intents to evaluate competency of four state-of-the-art deep CNNs namely AlexNet, GoogLeNet, ResNet50 and DenseNet201 using fine tuning and deep CNNs as feature extractors to classify sugarcane and non-sugarcane areas from Sentinel-2 data. The results of the research are expressive on CaneSat dataset. It shows that the CNN model performs significantly good producing 88.46% accuracy, whereas all deep networks exhibit more than 73.00% overall accuracy. When used as feature extractors, ResNet50 and DenseNet201 outperform all other models with precision of 85.65% and 87.70%, respectively. Noticeably, the results indicate that 2D CNN model and features extracted using CNNs with SVM classifier are efficient methods for sugarcane classification from Sentinel-2 time series data in peninsular zone of India.

Keywords

• Multi-temporal remote sensing images
• Sugarcane classification
• Convolutional neural network
• Deep network