HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data

Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0.5m/px. Segmenting SAR data still requires skilled personnel, limiting the potential for large-scale use. We show that it is possible to automatically and reliably perform urban scene segmentation from next-gen resolution SAR data (0.15m/px) using deep neural networks (DNNs), achieving a pixel accuracy of 95.19% and a mean IoU of 74.67% with data collected over a region of merely 2.2km${}^2$. The presented DNN is not only effective, but is very small with only 63k parameters and computationally simple enough to achieve a throughput of around 500Mpx/s using a single GPU. We further identify that additional SAR receive antennas and data from multiple flights massively improve the segmentation accuracy. We describe a procedure for generating a high-quality segmentation ground truth from multiple inaccurate building and road annotations, which has been crucial to achieving these segmentation results

Developing a method to improve SAR change detection under varying illumination angles

Change detection is an important field of application for remote sensing. While many studies rely on optical data, Synthetic Aperture Radar brings the advantage of its potential operability being independent from natural illumination and weather conditions. Particularly in time sensitive situations, this can be a great advantage. However, SAR change detection evokes some challenges that may not be seen in other sensor technologies. One of these challenges includes its dependability on a consistent illumination geometry when applying known 2D change detection methods. False alarms increase with growing difference between the illumination angles of a reference and test image. The characteristics of these false alarms have been studied in this thesis and it has been revealed that they predominantly occur as pairs caused by a single object. This phenomenon has been exploited to develop an algorithm which checks elements of change detection on potential pairs and removes them if a certain agreement between the elements of the pairs is met. The results have shown that the method is able to greatly reduce the occurrence of false alarms, especially in large angular deviations. In smaller angles where current methods perform well, the algorithm retains a majority of the detected changes which are mostly attributable to true changes. Challenges persist in intermediate angles, where false alarms are ample, but pairs are not as pronounced. Nevertheless, the method managed to significantly reduce the false alarm rate in all acquisitions in a circle while retaining the detection rate to a great part

A back-projection tomographic framework for VHR SAR image change detection

Information on 3-D structure expands the scope of change detection applications, for example, in urban studies, human activity, and forest monitoring. Current change detection methods do not fully consider the specifics of SAR data or the properties of the corresponding image focusing techniques. We propose a three-stage method complementing the properties of 2-D and 3-D very high-resolution (VHR) synthetic aperture radar imagery to improve the performance of 2-D only approaches. The method takes advantage of back-projection tomography to ease translation of the 2-D location of the targets into their corresponding 3-D location and vice versa. Detection of changes caused by objects with a small vertical extent is based on the corresponding backscatter difference, while changes caused by objects with a large vertical extent are detected with both backscatter and height difference information combined in a conditional random field. Using multitemporal images, the kappa coefficient improved by a factor of two in comparison with traditional schemes