#6368. BTS: a binary tree sampling strategy for object identification based on deep learning

November 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Geography, Planning and Development;
Library and Information Sciences;
Information Systems;
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Abstract:
Object-based convolutional neural networks (OCNNs) have achieved great performance in the field of land-cover and land-use classification. Studies have suggested that the generation of object convolutional positions (OCPs) largely determines the performance of OCNNs. Optimized distribution of OCPs facilitates the identification of segmented objects with irregular shapes. In this study, we propose a morphology-based binary tree sampling (BTS) method that provides a reasonable, effective, and robust strategy to generate evenly distributed OCPs. The proposed BTS algorithm consists of three major steps: 1) calculating the required number of OCPs for each object, 2) dividing a vector object into smaller sub-objects, and 3) generating OCPs based on the sub-objects. Taking the object identification in land-cover and land-use classification as a case study, we compare the proposed BTS algorithm with other competing methods.
Keywords:
convolutional neural network; deep learning; object convolutional position; Object identification

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