Open Access

ARTICLE

Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

by Ying Li^1,2, Guanghong Gong¹, Dan Wang¹, Ni Li^1,3,*

1 School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
2 Shenyuan Honors College, Beihang University, Beijing, 100191, China
3 State Key Laboratory of Virtual Reality Technology and Systems, Beijing, 100191, China

* Corresponding Author: Ni Li. Email:

Computer Modeling in Engineering & Sciences 2023, 136(3), 2237-2265. https://doi.org/10.32604/cmes.2023.025193

Received 27 June 2022; Accepted 21 November 2022; Issue published 09 March 2023

Abstract

There are two types of methods for image segmentation. One is traditional image processing methods, which are sensitive to details and boundaries, yet fail to recognize semantic information. The other is deep learning methods, which can locate and identify different objects, but boundary identifications are not accurate enough. Both of them cannot generate entire segmentation information. In order to obtain accurate edge detection and semantic information, an Adaptive Boundary and Semantic Composite Segmentation method (ABSCS) is proposed. This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances. It includes adaptively dividing and modifying the aerial images with the proposed principles and methods, using the deep learning method to semantic segment and preprocess the small divided pieces, using three traditional methods to segment and preprocess original-size aerial images, adaptively selecting traditional results to modify the boundaries of individual objects in deep learning results, and combining the results of different objects. Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset, and their results are analyzed qualitatively and quantitatively. The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method. This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.

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