An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion

Abstract

Over one hundred spatiotemporal fusion algorithms have been proposed, but convolutional neural networks trained with large amounts of data for spatiotemporal fusion have not shown significant advantages. In addition, no attention has been paid to whether fused images can be used for change detection. These two issues are addressed in this work. A new dataset consisting of nine pairs of images is designed to benchmark the accuracy of neural networks using one-pair spatiotemporal fusion with neural-network-based models. Notably, the size of each image is significantly larger compared to other datasets used to train neural networks. A comprehensive comparison of the radiometric, spectral, and structural losses is made using fourteen fusion algorithms and five datasets to illustrate the differences in the performance of spatiotemporal fusion algorithms with regard to various sensors and image sizes. A change detection experiment is conducted to test if it is feasible to detect changes in specific land covers using the fusion results. The experiment shows that convolutional neural networks can be used for one-pair spatiotemporal fusion if the sizes of individual images are adequately large. It also confirms that the spatiotemporally fused images can be used for change detection in certain scenes.