Hybrid Feature Embedding For Automatic Building Outline Extraction

TL;DR

This paper introduces a hybrid CNN-Transformer model with an active contour component and a triple-branch decoder for precise building outline extraction from aerial images, significantly improving accuracy over baseline models.

Contribution

It presents a novel combined CNN-Transformer architecture with a triple-branch decoder and active contour integration for enhanced building outline detection.

Findings

Achieved 91.1% mIoU on Vaihingen dataset

Achieved 83.8% mIoU on Bing huts dataset

Outperformed baseline models in accuracy

Abstract

Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment. However, traditional CNN model cannot recognize contours very precisely from original images. In this paper, we proposed a CNN and Transformer based model together with active contour model to deal with this problem. We also designed a triple-branch decoder structure to handle different features generated by encoder. Experiment results show that our model outperforms other baseline model on two datasets, achieving 91.1% mIoU on Vaihingen and 83.8% on Bing huts.