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

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing.

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology

Earth Resources: A continuing bibliography with indexes (Issue 37)

This bibliography lists 512 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1983. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

A multisquint framework for change detection in high-resolution multitemporal SAR images

Change detection from multitemporal synthetic aperture radar (SAR) images enables mapping applications for earth environmental observation, human activity monitoring, and urban studies. We expand the use of SAR data beyond single look processing to include the spatial response of targets. This information is derived from a multisquint framework similar to beamforming. To preserve changes detected at nominal resolution, a three-stage change detector exploiting single-look and multisquint processing mode is proposed to mitigate false alarms caused by image artifacts typically found in high-resolution SAR imagery and urban scenarios. After applying the proposed method to multitemporal images, the false alarm rate was reduced by a factor 3, while preserving 95% of the detection rate offered by traditional schemes