A New Texture Aware—Seed Demand Enhanced Simple Non-Iterative Clustering (ESNIC) Segmentation Algorithm for Efficient Land Use and Land Cover Mapping on Remote Sensing Images
Change detection in Land Use and Land Cover (LULC) on remote sensing images is essential for urban planning, disaster risk management, climate change monitoring, and biodiversity conservation. Precise detection of these changes is heavily impacted by the classification accuracy of the LULC types whi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10806649/ |
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| Summary: | Change detection in Land Use and Land Cover (LULC) on remote sensing images is essential for urban planning, disaster risk management, climate change monitoring, and biodiversity conservation. Precise detection of these changes is heavily impacted by the classification accuracy of the LULC types which can be improved significantly by addressing the misclassification errors arising due to similar spectral LULC types and overlapping LULC regions. This paper proposes a texture-aware and seed-demanding Enhanced Simple Non-Iterative Clustering (ESNIC) segmentation algorithm and Boundary-Specific Two-Level (BSTL) classification approach that reduces misclassification rates due to similar spectral signatures and minimizes computational redundancy. Incorporating texture features extracted through the Gray-Level Co-occurrence Matrix along with spectral information in the proposed ESNIC segmentation algorithm improves the ability to distinguish between different LULC types that share the same spectral value. The seed demanding ESNIC segmentation approach seeds are strategically placed based on the content adaptation approach rather than being uniformly distributed throughout the image which reduces segmentation time, providing a substantial advantage for large-scale land cover mapping. A BSTL classification approach that synergistically combines the Support Vector Machine’s ability to effectively handle high dimensional data with the k-Nearest Neighbor’s ability to handle irregular data is used. This study is assessed in terms of Overall Accuracy(OA), Producer Accuracy, User Accuracy, kappa coefficients (K), Root Mean Square Errors (RMSE), and F1 scores. Results indicate that the proposed ESNIC-BSTL (OA = 97.18%, <inline-formula> <tex-math notation="LaTeX">$\text {K} = 0.96$ </tex-math></inline-formula> and RMSE =0.1311) approach provides better accuracy than SNIC-SVM (94.42%, 0.92, and 0.1422) and SNIC- BSTL (95.78%, 0.94 and 0.1362). |
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| ISSN: | 2169-3536 |