Deep learning based crop-type mapping using SAR and optical data fusion

Abstract

Accurate crop maps are essential in various applications related to food security management activities. Remote Sensing is the primary data source for land-use and land-cover monitoring applications. However, high spectral, spatial, and temporal variations of crop types during their phonological stages make crop mapping challenging. According to the complementary behaviors of optical and radar data, a deep learning-based feature-level fusion strategy is proposed for accurate crop-type mapping. The proposed method uses Sparse Auto-Encoders (SAE) as its base classifier and can involve spatial information in two data-preparation and post-processing steps. Involving spatial information in the classification process can significantly improve crop mapping. The post-processing step uses the guided filter as an edge-aware filtering process that uses field boundary information that implicitly exists in data. Accuracy analysis was performed using two pairs of RapidEye and UAVSAR data from two agricultural areas in Canada. The suitability and higher performance of the proposed method were demonstrated compared to traditional machine learning and common decision-level fusion methods. Accuracy metrics were improved by at least 3% and 1% in two datasets. Class F-scores were also significantly enhanced in oat and wheat classes (up to 20%).