Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms. Issue 11 (22nd November 2021)

• Record Type: Journal Article
• Authors: Adeli, Sarina; Salehi, Bahram; Mahdianpari, Masoud; Quackenbush, Lindi J.; Chapman, Bruce
• Is Part Of: Earth and space science. Volume 8:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-22
• Subjects: NISAR -- machine learning -- random forest -- SVM -- classification -- wetland mapping; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2021EA001742
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands' ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA‐ISRO SAR (NISAR) mission. In this study, the capability of L‐band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana, is investigated using two object‐based machine learning approaches: Support vector machine (SVM) and random forest (RF). L‐band Unmanned Aerial Vehicle SAR (UAVSAR) data are exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean‐shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L‐band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman‐Durden, and Aghababaee features have the highest contribution to the overall accuracy. Plain Language Summary: By illuminating the surface, SAR signals can provide meaningful information on the shape, geometry, and roughness of the surface. In particular, polarimetric decompositions bring a measure of the relative contribution of backscatter from different scattering mechanisms that can be used for wetland delineations, classification, and monitoring. Given the availability of various polarimetric decompositions, the selection of appropriate decomposition based on the application and SAR sensor configuration is crucial. In this study, we investigated the performance of various polarimetric decompositions for delineating wetlands classes over Yucatan Lake in Louisiana. The adopted machine learning classification workflow was applied to the L‐band simulated NISAR data that are acquired by the UAVSAR platform to evaluate the performance of planned L‐band NISAR data. Our investigation showed that H/A/ALPHA, Freeman‐Durden, and Aghababaee features have the highest contribution to the overall accuracy. Key Points: High spatial resolution Earth Observation (EO) data and machine learning techniques have provided opportunities for preservation of wetlands L‐band simulated NISAR was captured with UAVSAR as a proxy for evaluating the planned NISAR for application of wetland monitoring Using 84 polarimetric features and support vector machine and random forest classifiers, overall accuracies of 74.33% and 83.93% were obtained