Segment-based bag of visual words model for urban land cover mapping using polarimetric SAR data

Abstract

Producing high-resolution urban land cover maps is essential for decision-making and urban management. In this regard, Synthetic Aperture Radar (SAR), especially Polarimetric SAR (PolSAR), has served as a valuable data source to fulfill this task. Direct conversion of low-level features into high-level Land Cover (LC) concept may reduce the final classification accuracy. Therefore, mid-level representation models, such as Bag of Visual Words (BOVW), were employed to resolve the existing semantic gap challenge. In this paper, a Segment-based BOVW (Seg-BOVW) model was developed for urban land cover classification using PolSAR data. To this end, two PolSAR data over San Francisco Bay (SFB) and Flevoland (FL) acquired by RADARSAT-2 were employed to comprehensively evaluate the Seg-BOVW model's performance. First, to exploit the full potential of PolSAR data, 169 low-level features in four categories: (1) original, (2) polarimetric, (3) texture, and (4) decomposition features were extracted. Afterward, a Multi-Objective Genetic Algorithm (MOGA) was implemented to investigate the importance of low-level features for urban land cover mapping. This step resulted in selecting 14 and 6 low-level features, as the high contributing features, for SFB and FL datasets, respectively. The Seg-BOVW model achieved significant overall accuracies of 96.02% and 98.82% for SFB and FL, respectively, indicating the high potential of the proposed method for urban land cover classification. Furthermore, a comparison with other well-known algorithms of Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) was made, suggesting the capability of the Seg-BOVW model to improve the urban land cover classification results. Finally, the Seg-BOVW model was tested with two other PolSAR datasets acquired with different sensors over SFB to examine its applicability with different datasets.