Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
This work addresses urban monitoring for remote sensing applications, but it is incremental as it applies existing methods to new data.
The paper tackles urban change detection using multispectral satellite images by introducing a new dataset and two convolutional neural network architectures, achieving competitive performance on the new benchmark.
The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.