Cropland Change Detection With Harmonic Function and Generative Adversarial Network

Abstract

Time-series image change detection is one of the most challenging tasks to remote sensing society. Due to complex phenological patterns of cropland, it is difficult to design an efficient strategy for cropland change detection. In this work, an integrated framework is proposed to perform change detection with a limited number of training samples. There are two improvements in this proposed cropland change detection method: 1) the harmonic function is utilized to fill the missing data within a time-series image stack by considering phenological patterns of cropland and 2) the CropGAN was developed to generate realistic samples for training data set enrichment. Compared to the traditional change detection methods, the proposed strategy able to detect different kinds of cropland changes even with few number of samples. Experiments on a Landsat time-series image stack demonstrated that the proposed CropGAN can significantly improve change detection accuracies, given a limited number of labeled samples.