Harnessing Commercial Satellite Imagery, Artificial Intelligence, and High Performance Computing to Characterize Ice-wedge Polygonal Tundra

Abstract:

We developed a high throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial very high spatial resolution (VHSR) multispectral (MS) satellite imagery. Specifically, we applied a region-based CNN object instance segmentation algorithm Mask RCNN as the DLCNN architecture, to detect IWPs from eight-band Worldview-02 VHSR satellite imagery in North slope of Alaska. The central goal of our study was to understand the impact on choosing the optimal three-band combination in DLCNN model prediction and systematically expound the model interoperability across varying tundra types (tussock, non-tussock, and sedge).We tasked multiple cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.96, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17–0.23 and 0.20–0.21 %, respectively for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels. Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types.