Building Detection in High-Resolution Remote Sensing Images by Enhancing Superpixel Segmentation and Classification Using Deep Learning Approaches

Author:

Benchabana Ayoub12ORCID,Kholladi Mohamed-Khireddine13,Bensaci Ramla4ORCID,Khaldi Belal4ORCID

Affiliation:

1. Department of Computer Science, University of El Oued, El Oued 39000, Algeria

2. Laboratory of Operator Theory and EDP: Foundations and Application, University of El Oued, El Oued 39000, Algeria

3. MISC Laboratory of Constantine 2, University of Constantine 2, El Khroub 25016, Algeria

4. Laboratory of Artificial Intelligence and Data Science, University of Kasdi Merbah Ouargla, PB. 511., Ouargla 30000, Algeria

Abstract

Accurate building detection is a critical task in urban development and digital city mapping. However, current building detection models for high-resolution remote sensing images are still facing challenges due to complex object characteristics and similarities in appearance. To address this issue, this paper proposes a novel algorithm for building detection based on in-depth feature extraction and classification of adaptive superpixel shredding. The proposed approach consists of four main steps: image segmentation into homogeneous superpixels using a modified Simple Linear Iterative Clustering (SLIC), in-depth feature extraction using an variational auto-encoder (VAE) scale on the superpixels for training and testing data collection, identification of four classes (buildings, roads, trees, and shadows) using extracted feature data as input to an Convolutional Neural Network (CNN), and extraction of building shapes through regional growth and morphological operations. The proposed approach offers more stability in identifying buildings with unclear boundaries, eliminating the requirement for extensive prior segmentation. It has been tested on two datasets of high-resolution aerial images from the New Zealand region, demonstrating superior accuracy compared to previous works with an average F1 score of 98.83%. The proposed approach shows potential for fast and accurate urban monitoring and city planning, particularly in urban areas.

Publisher

MDPI AG

Subject

Building and Construction,Civil and Structural Engineering,Architecture

Reference35 articles.

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