With more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs, due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the “dialectical” structure of GAN frameworks. As a demonstration, a typical example will be shown, where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). A new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network—Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.

Users of remote sensing images analyzing land cover characteristics are very much interested in classification schemes that define a consistent set of target categories. Up to now, a number of established classification schemes are mainly being used by interpreters of medium-resolution optical satellite images focusing on large-scale land cover. In contrast, we concentrate in this publication on the definition of a new classification scheme for high-resolution synthetic aperture radar (SAR) images that are mostly taken over built-up areas. Here, we can see many small details of buildings, industrial facilities, and infrastructure that have to be classified. However, the appearance of details in high-resolution SAR images is often difficult to understand for human observers, and, therefore, calls for an automated semantic annotation of the target objects that has to follow a number of specific scientific guidelines. We demonstrate that a selection of representative SAR images with subsequent feature extraction and relevance feedback classification during the generation of a classification scheme leads to a reliable definition of a new high-resolution multi-level SAR image classification scheme that can be applied globally for semantic annotation in an automated chain.

In this paper, we present data analytics for a quantitative analysis in a rapid mapping scenario applied for damage assessment of the 2013 floods in Germany and the 2011 tsunami in Japan. These scenarios are created using preand postdisaster TerraSAR-X images and a semi-automated processing chain. All our dataset is tiled into patches and Gabor filters are applied as a primitive feature extraction method to each patch separately. A support vector machine with relevance feedback is implemented in order to group the features into categories. Once all categories are identified, these are semantically annotated using reference data as ground truth. In our investigation, nondamaged and damaged categories were retrieved with their specific taxonomies defined using our previous hierarchical annotation scheme. The classifier supports rapid mapping scenarios (e.g., floods in Germany and tsunami in Japan) and interactive mapping generation. The quantitative damages can be assessed by: 1) flooded agricultural areas (21.66% in the case of floods in Germany and 4.15% in the case of tsunami in Japan) and destroyed aquaculture (2.33% in the case of tsunami in Japan); 2) destroyed transportation infrastructures, such as airport (50% in case tsunami in Japan), bridges, and roads.; and 3) debris that appears in postdisaster images (3.81% in the case of tsunami after the aquaculture was destroyed). The first analysis envisages the floods of Elbe river in June 2013: 30% of the investigated area, about , including agricultural land, forest, river, and some residential and industrial areas close to the river, was covered by water. The second analysis, considering an area of affected by the tsunami, led us to conclude that 3 months after the tsunami, some of the categories returned to previous functionality-the airport, others return to partial functionality such as isolated residents, and some were totally destroyed-the aquaculture. The flooded area was about . The proposed approach goes beyond a simple annotation of the data and provides an intermediate product in order to produce a relevant visual analytics representation of the data. This makes it easier to compare datasets and different quantitative findings in a meaningful manner, accessible both to experts and regular users. Our paper presents an interactive and automatic, fast processing method applicable to large and complex datasets (such as image time series). In addition to enhancing the information content extraction (number of identified categories), this approach enables the discovery and analysis of these categories. The novelty of this paper resides in that this is the first time data analytics have been run on a large dataset and for different scenarios using a semiautomated processing chain.

The increased availability of high-resolution synthetic aperture radar (SAR) satellite images has led to new civil applications of these data. Among them is the systematic classification of land cover types based on the patterns of settlements or agriculture recorded by SAR imagers, in particular the identification and quantification of temporal changes. A systematic (re)classification shall allow the assignment of continuously updated semantic content labels to local image patches. This necessitates a careful selection of well-defined and discernible categories being contained in the image data that have to be trained and validated. These steps are well-established for optical images, while the peculiar imaging characteristics of SAR sensors often prevent a comparable approach. Especially, the vast range of SAR imaging parameters and the diversity of local targets impact the image product characteristics and need special care. In the following, we present guidelines and practical examples of how to obtain reliable image patch classification results for time series data with a limited number of given training data. We demonstrate that one can avoid the generation of simulated training data if we decompose the classification task into physically meaningful subsets of characteristic target properties and important imaging parameters. Then, the results obtained during training can serve as benchmarking figures for subsequent image classification. This holds for typical remote sensing examples such as coastal monitoring or the characterization of urban areas where we want to understand the transitions between individual land cover categories. For this purpose, a representative dataset can be obtained from the authors. A final proof of our concept is the comparison of classification results of selected target areas obtained by rather different SAR instruments. Despite the instrumental differences, the final results are surprisingly similar.

In this paper, we propose a promising approach for the application-oriented content classification of space-borne radar imagery that presents an interesting alternative to popular current machine learning algorithms. In the following, we consider the problem of unsupervised feature-free satellite image classification as an explainable data mining problem for regions with no prior information. Three important issues are addressed here: explainability, unsupervision and feature-independence. There is an increasing demand towards explainable machine learning models as they strive to meet the “right to explanation”. The importance of feature-free classification stems from the problem that different classification outcomes are obtained from using different features and the complexity of computing sophisticated image primitive features. Developing unsupervised discovery techniques helps overcome the limitations in object discovery due to the lack of labelled data and the dependence on features. In this paper, we demonstrate the applicability of the Latent Dirichlet Allocation (LDA) model, one of the most established unsupervised probabilistic methods, in discovering the latent structure of synthetic aperture radar (SAR) data. The idea is to use LDA as an explainable data mining tool to discover scientifically explainable semantic relations. The suitability of the approach as an explainable model is discussed and interpretable topic representation maps are produced which practically demonstrate the idea of “interpretability” in the explainable machine learning paradigm. LDA discovers the latent structure in the data as a set of topics. We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class.