Crop Type Classification using Multi-temporal Sentinel-2 Satellite Imagery: A Deep Semantic Segmentation Approach

Abstract

Crop Type classification using Semantic Segmentation and remote sensing data is an important tool for decision-making related to precision agriculture. Such classification remains an unsolved challenge due to the choice of landscape, processing methodology and selected satellite imagery and its optical features, and most importantly the availability and usage of such datasets in a developing country like Pakistan. State-of-the-art semantic segmentation models lack in processing the temporal dimension of time series imagery and evident solution to process multi-spectral bands available in the satellite imagery. We propose a methodology to overcome these shortcomings by selecting appropriate band combinations for crop type classification and treating time series visual data as a single image. The proposed methodology is evaluated on the data set of six different crops collected from National Agriculture Research Center (NARC) Islamabad. The experimental results yield 85% accuracy for classifying various crop types based on the evaluation of five different semantic segmentation models. The code and the trained models are available at https://github.com/asimniazi63/crop-type-narc for other researchers working in the same domain.