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Segment-based bag of visual words model for urban land cover mapping using polarimetric SAR data
- Source :
- Advances in Space Research. 70:3784-3797
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
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.
- Subjects :
- Synthetic aperture radar
Atmospheric Science
Artificial neural network
Computer science
business.industry
Aerospace Engineering
Astronomy and Astrophysics
Pattern recognition
Land cover
Convolutional neural network
Support vector machine
Geophysics
Space and Planetary Science
Bag-of-words model in computer vision
General Earth and Planetary Sciences
Cover (algebra)
Artificial intelligence
business
Semantic gap
Subjects
Details
- ISSN :
- 02731177
- Volume :
- 70
- Database :
- OpenAIRE
- Journal :
- Advances in Space Research
- Accession number :
- edsair.doi...........439d782583eac6ab8340d8e9f773fe28