This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.
Due to the limited amount of available data, most of these methods use various techniques based on transfer learning, taking as a starting point networks that have been trained on larger datasets for different problems. This is limiting in many ways, as it assumes similarities between these datasets and the relevant change detection data. For example, most of the networks have been trained on RGB images, and cannot be transfered for SAR or multispectral images, which is the case of the dataset presented in @cite_13 . These methods also avoid end-to-end training, which tends to have better results for successfully trained systems. For this reason, the focus of this paper is algorithms that are able to learn solely from the available change detection data, and can therefore be applied to any available datasets.
@cite_13
2896365540
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.