The RADARSAT Constellation Mission Core Applications: First Results

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase with the performance evaluation being currently active. This evaluation aims to confirm that the innovative design of the mission’s synthetic aperture radar (SAR) meets the expectations of intended users. In this study, we provide an overview of initial results obtained for three high-priority applications; flood mapping, sea ice analysis, and wetland classification. In our study, the focus is on results obtained using not only linear polarization, but also the adopted Compact Polarimetric (CP) architecture in RCM. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba, suggesting smooth continuity between the two satellite missions for operational flood mapping. Visual analysis of coincident RCM CP and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. A statistical analysis using selected sea ice samples confirmed the increased contrast between thin and both rough and deformed ice in CP SAR. This finding is expected to enhance Canadian Ice Service’s (CIS) operational visual analysis of sea ice in RCM SAR imagery for ice chart production. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87. Marsh presented the highest user’s and producer’s accuracies (87.77% and 82.08%, respectively) compared to fog, fen, and swamp.

Sentinel-1 Spatiotemporal Simulation Using Convolutional LSTM for Flood Mapping

The synthetic aperture radar (SAR) imagery has been widely applied for flooding mapping based on change detection approaches. However, errors in the mapping result are expected since not all land-cover changes are flood-induced, and those changes are sensitive to SAR data, such as crop growth or harvest over agricultural lands, clearance of forested areas, and/or modifications on the urban landscape. This study, therefore, incorporated historical SAR images to boost the detection of flood-induced changes during extreme weather events, using the Long Short-Term Memory (LSTM) method. Additionally, to incorporate the spatial signatures for the change detection, we applied a deep learning-based spatiotemporal simulation framework, Convolutional Long Short-Term Memory (ConvLSTM), for simulating a synthetic image using Sentinel One intensity time series. This synthetic image will be prepared in advance of flood events, and then it can be used to detect flood areas using change detection when the post-image is available. Practically, significant divergence between the synthetic image and post-image is expected over inundated zones, which can be mapped by applying thresholds to the Delta image (synthetic image minus post-image). We trained and tested our model on three events from Australia, Brazil, and Mozambique. The generated Flood Proxy Maps were compared against reference data derived from Sentinel Two and Planet Labs optical data. To corroborate the effectiveness of the proposed methods, we also generated Delta products for two baseline models (closest post-image minus pre-image and historical mean minus post-image) and two LSTM architectures: normal LSTM and ConvLSTM. Results show that thresholding of ConvLSTM Delta yielded the highest Cohen’s Kappa coefficients in all study cases: 0.92 for Australia, 0.78 for Mozambique, and 0.68 for Brazil. Lower Kappa values obtained in the Mozambique case can be subject to the topographic effect on SAR imagery. These results still confirm the benefits in terms of classification accuracy that convolutional operations provide in time series analysis of satellite data employing spatially correlated information in a deep learning framework.

Permafrost Dynamics and Degradation in Polar Arctic From Satellite Radar Observations, Yamal Peninsula

We investigate permafrost surface features revealed from satellite radar data in the Siberian arctic at the Yamal peninsula. Surface dynamics analysis based on SRTM and TanDEM-X DEMs shows up to 2 m net loss of surface relief between 2000 and 2014 indicating a highly dynamic landscape. Surface features for the past 14 years reflect an increase in small stream channels and a number of new lakes that developed, likely caused by permafrost thaw. We used Sentinel-1 SAR imagery to measure permafrost surface changes. Owing to limited observation data we analyzed only 2 years. The InSAR time-series has detected surface displacements in three distinct spatial locations during 2017 and 2018. At these three locations, 60–120 mm/yr rates of seasonal surface permafrost changes are observed. Spatial location of seasonal ground displacements aligns well with lithology. One of them is located on marine sediments and is linked to anthropogenic impact on permafrost stability. Two other areas are located within alluvial sediments and are at the top of topographic elevated zones. We discuss the influence of the geologic environment and the potential effect of local upwelling of gas. These combined analyses of InSAR time-series with analysis of geomorphic features from DEMs present an important tool for continuous process monitoring of surface dynamics as part of a global warming risk assessment.

An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

Synthetic Aperture Radar (SAR) images show promising results in monitoring maritime activities. Recently, Deep learning-based object detection techniques have impressive results in most detection applications but unfortunately there are challenging problems such as difficulty of detecting multiple ships, especially inshore ones. In this paper, a three-step ship detection process is described and a reliable and sensitive hybrid deep learning model is proposed as an efficient classifier in the middle step. The proposed model combines the finetuned Inception-Resnet-V2 model and the Long Short Term Memory model in two different approaches: parallel approach and cascaded approach. In experiments, the region proposal algorithm and the Non-Maxima suppression algorithm are applied in the first and last step in the three-step detection process. The comparative results show that the proposed approach in cascaded form outperforms the competitive recent state-of-the-art approaches by enhancement up to 16.3%, 16.5%, and 18.9% in terms of recall, precision and mean average precision, respectively. Moreover, the proposed approach shows high relative sensitivity for challenged cases of both inshore and offshore scenes by enhancement ratios up to 81.88% and 24.58%, respectively in recall perspective.