High precision building detection from satellite imagery with a novel SDBN-HCWO method

Abstract

Detecting buildings from satellite imagery presents challenges related to computational efficiency, model adaptation, and occlusion. This paper introduces a novel method called the Secant Deep Belief Network-Hyperbolic Cosine Whale Optimization (SDBN-HCWO) for building detection in satellite images. The research utilizes the SDBN-HCWO method to enhance building detection accuracy in satellite images. It addresses challenges like computational efficiency, occlusion, and dataset adaptation. The method integrates a multi-layer structure, including Hyperbolic Cosine Prey Encircling for edge identification, Shrinking Encircle for optimal edge linking, and Secant Object Detection for accurate identification. Additionally, a Densely Connected Convolutional Network (DCCN) and Depth-wise Separable Convolution (DSC) optimize feature extraction, reducing computational costs. The model is evaluated on both quantitative and qualitative metrics, ensuring high accuracy and low false positive rates. The research findings demonstrate that the SDBN-HCWO method significantly improves building detection accuracy in satellite imagery. It enhances detection efficiency by integrating Discrete Latent Deep Reinforcement Learning and a bubble-net mechanism, reducing false positives by 58 %. The model outperforms conventional approaches, achieving an 18 % increase in PSNR, 34 % rise in CA, and 19 % reduction in training time. High AP scores (90.40 %–92.67 %) confirm its reliability, though challenges persist in medium-damage detection. It surpasses YOLOv3, YOLOv4, and Faster R-CNN in accuracy and efficiency. This research significantly advances building detection in satellite imagery, facilitating more accurate urban planning, disaster response, and environmental monitoring.