No abstract

Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images.

Automatic target recognition (ATR) algorithms have been successfully used for vehicle classifcation in synthetic aperture radar (SAR) images over the past few decades. For this application, however, the scarcity of labeled data is often a limiting factor for supervised approaches. While the advent of computer-simulated images may result in additional data for ATR, there is still a substantial gap between synthetic and measured images. In this paper, we propose the so-called adaptive target enhancer (ATE), a tool designed to automatically delimit and weight the region of an image that contains or is affected by the presence of a target. Results for the publicly released Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset show that, by defning regions of interest and suppressing the background, we can increase the classifcation accuracy from 68% to 84% while only using artifcially generated images for training.

<p>Climate change and the resulting accelerating melt on the Greenland and Antarctic ice sheets are causing dramatic and irreversible changes at a global scale, significantly contributing to sea-level rise. In this scenario, monitoring the evolution of diagenetic snow facies can provide valuable insights to better comprehend climate-related variables and trends. Previous studies of the Greenland ice sheet led to the definition of four main snow facies, depending on the amount of snow melt and on the properties of the snow pack itself: the inner dry snow zone, where melt does not occur; the percolation zone, where a limited amount of melt per year occurs, leading to the generation of larger snow grains and the formation of small ice structures; the wet snow zone, where a substantial part of the snow melt drains off during summer and is characterized by the presence of multiple ice layers; and the outer ablation zone, where the previous year accumulation completely melts during summer, resulting in a surface of bare ice and surface moraine. By exploiting X-band TanDEM-X interferometric synthetic aperture radar (InSAR) acquisitions, previous works explored the idea of classifying different snow facies of the Greenland ice sheet utilizing an unsupervised machine learning clustering approach. The analysis was performed using data acquired in winter 2010/2011 only, under the assumption of stable climatic conditions and similar acquisition geometries. In this paper, we further investigate the evolution of the snow facies of Greenland throughout the last decade of TanDEM-X observations, proposing unsupervised machine learning strategies for snow facies characterization by using InSAR features such as backscatter, volume decorrelation, the incidence angle and height of ambiguity. We use TanDEM-X data acquired during the winter of 2010/2011, 2015/2016, 2016/2017, 2020/2021, and 2021/2022, where full or partial coverage of the Greenland ice sheet is available. The challenges and caveats of such approaches for different image acquisition geometries will be presented. Finally, the potential of TanDEM-X for investigating large-scale interannual changes in the dry snow zone over Greenland will be investigated as well.  </p>