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- W3202759083 abstract "Textural measures of target objects have been studied to increase the accuracy of mapping sparse woodlands on satellite images. Kernel-based granulometry analyses were used to calculate BCM, NDC, RR, FI, H, and DI on QuickBird, RapidEye, and ASTER images. The Jeffrey-Matusita distance was used for analyzing the separability of the training ROIs. Finally, supervised segmentation was accomplished using the probabilistic maximum-likelihood rule on the measures. The results indicated that BCM and NDC using 5 × 5 and 7 × 7 kernels are more successful for QuickBird and RapidEye images, considering the local co-occurrence of the openings. On ASTER images, the results were considerable because of the convolution effects, defining the granules. Contextual information of the images increased 20%–30% to the accuracy heuristics of overall accuracy and kappa coefficient, 98% for the ASTER data set. The method is easy to retrieve and is helpful for the segmentation of the scene of fine-grain textural patterns." @default.
- W3202759083 created "2021-10-11" @default.
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- W3202759083 date "2022-01-01" @default.
- W3202759083 modified "2023-09-25" @default.
- W3202759083 title "Kernel-based granulometry of textural pattern measures on satellite imageries for fine-grain sparse woodlands mapping" @default.
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- W3202759083 doi "https://doi.org/10.1016/b978-0-323-89861-4.00016-6" @default.
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