Application of 1D CNN and 3D CNN for Coarse Resolution Satellite Image Classification in Extensive Area Land Cover Mapping

Convolutional Neural Networks (CNNs) have been applied effectively to classify high to medium-resolution imageries, especially for local scale land cover mapping, and attained high accuracy by outperforming the conventional machine learning techniques as the models incorporate spatial information as well as spectral reflectance values to differentiate objects.However, few studies have explored the effectiveness of CNN models for coarse-resolution satellite image classification. In this work, therefore, we applied 1D CNN and 3D CNN for time-series, coarse resolution (1km) FY-3C image classification in extensive area land cover mapping of a part of Eastern and North-East Africa.The result indicates both 1D CNN and 3D CNN models achieved high overall accuracy (OA >=85), although the former outperformed the latter significantly by 2-4%, indicating the superiority of pixel-based classification in time-series coarse resolution image classification.