Superresolution Land Cover Mapping Using a Generative Adversarial Network

Superresolution mapping (SRM) is a commonly used method to cope with the problem of mixed pixels when predicting the spatial distribution within low-resolution pixels. Central to the popular SRM method is the spatial pattern model, which is utilized to represent the land cover spatial distribution within mixed pixels. The use of an inappropriate spatial pattern model limits such SRM analyses. Alternative approaches, such as deep-learning-based algorithms, which learn the spatial pattern from training data through a convolutional neural network, have been shown to have considerable potential. Deep learning methods, however, are limited by issues such as the way the fraction images are utilized. Here, a novel SRM model based on a generative adversarial network (GAN), GAN-SRM, is proposed that uses an end-to-end network to address the main limitations of existing SRM methods. The potential of the proposed GAN-SRM model was assessed using four land cover subsets and compared to hard classification and several popular SRM methods. The experimental results show that of the set of methods explored, the GAN-SRM model was able to generate the most accurate high-resolution land cover maps

Self-supervised Multisensor Change Detection

Most change detection methods assume that pre-change and post-change images are acquired by the same sensor. However, in many real-life scenarios, e.g., natural disaster, it is more practical to use the latest available images before and after the occurrence of incidence, which may be acquired using different sensors. In particular, we are interested in the combination of the images acquired by optical and Synthetic Aperture Radar (SAR) sensors. SAR images appear vastly different from the optical images even when capturing the same scene. Adding to this, change detection methods are often constrained to use only target image-pair, no labeled data, and no additional unlabeled data. Such constraints limit the scope of traditional supervised machine learning and unsupervised generative approaches for multi-sensor change detection. Recent rapid development of self-supervised learning methods has shown that some of them can even work with only few images. Motivated by this, in this work we propose a method for multi-sensor change detection using only the unlabeled target bi-temporal images that are used for training a network in self-supervised fashion by using deep clustering and contrastive learning. The proposed method is evaluated on four multi-modal bi-temporal scenes showing change and the benefits of our self-supervised approach are demonstrated

Unsupervised Multiple-Change Detection in VHR Multisensor Images Via Deep-Learning Based Adaptation

none3siChange Detection (CD) using multitemporal satellite images is an important application of remote sensing. In this work, we propose a Convolutional-Neural-Network (CNN) based unsupervised multiple-change detection approach that simultaneously accounts for the high spatial correlation among pixels in Very High spatial Resolution (VHR) images and the differences in multisensor images. We accomplish this by learning in an unsupervised way a transcoding between multisensor multitemporal data by exploiting a cycle-consistent Generative Adversarial Network (CycleGAN) that consists of two generator CNN networks. After unsupervised training, one generator of the CycleGAN is used to mitigate multisensor differences, while the other is used as a feature extractor that enables the computation of multitemporal deep features. These features are then compared pixelwise to generate a change detection map. Changed pixels are then further analyzed based on multitemporal deep features for identifying different kind of changes (multiple-change detection). Results obtained on multisensor multitemporal dataset consisting of Quickbird and Pleiades images confirm the effectiveness of the proposed approach.noneSaha, Sudipan; Bovolo, Francesca; Bruzzone, LorenzoSaha, Sudipan; Bovolo, Francesca; Bruzzone, Lorenz