Land Cover Classification Based on Sentinel-2 Satellite Imagery Using Convolutional Neural Network Model: A Case Study in Semarang Area, Indonesia

摘要

Regional land use planning and monitoring remain an issue in many developing countries. Efficient solution for both tasks depended on remote sensing technology to capture and analyze remotely sensed data of the region of interest. Although a plethora of methods for land cover classification have been reported, the problem remained a challenging task in computer vision field. The advent of deep learning method in the past decade has been very instrumental to develop a robust method for land cover classification using satellite imagery as input. The objective of this paper was to present empiric results on using CNN as a land cover classifier model using Sentinel-2 spatial satellite imagery. Prior to model training, the input image representation was extracted using eCognition to produce texture, brightness, shape, and vegetation index. Land cover labeling followed the Land Cover Class in Medium Resolution Optical Imagery Interpretation document provided by Indonesian National Standardization Agency. The training of CNN model achieved 0.98 mean training accuracy and 0.98 mean testing accuracy. As comparison, the same data and same feature were trained with another model: Gradient Boosting Model (GBM). The results revealed that the training accuracy and testing accuracy with GBMs were 0.98 and 0.95 respectively. CNN model showed small improvement of the accuracy to classify land cover with the image feature (NDVI, Brightness, GLCM homogeneity and Rectangular fit).