Satellite Imagery Analysis for Crop Type Segmentation Using U-Net Architecture

Abstract

Analyzing satellite images plays a critical role in surveying and monitoring agricultural areas, enabling various applications such as precision agriculture, land use planning, and yield estimation. One of the crucial steps in these applications is accurate crop-type segmentation from satellite imagery. This task becomes challenging in the scenarios of smallholder farms due to their irregular field shapes, frequent cloud cover, small plot sizes, and a severe shortage of training data, which make it difficult to apply conventional machine learning methods effectively. In this research work, a technique for segmenting the crop types from the difficult scenario of smallholder farms based on fully convolutional encoder-decoder semantic segmentation architecture, U-Net, has been proposed and its performance has been compared with the traditional machine learning techniques. To evaluate the proposed approach, experimental assessments were conducted on the Kenya satellite imagery crop dataset. The proposed technique achieved an accuracy, precision, recall, and F1 score of 95.3%, 80.2%, 68.1%, and 73.6%, respectively. The results demonstrate that the U-Net model surpasses conventional image classification methods in accurately segmenting different crop types.