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

被引:7
|
作者
Benchabana, Ayoub [1 ,2 ]
Kholladi, Mohamed-Khireddine [1 ,3 ]
Bensaci, Ramla [4 ]
Khaldi, Belal [4 ]
机构
[1] Univ El Oued, Dept Comp Sci, El Oued 39000, Algeria
[2] Univ El Oued, Lab Operator Theory & EDP Fdn & Applicat, El Oued 39000, Algeria
[3] Univ Constantine 2, MISC Lab Constantine 2, El Khroub 25016, Algeria
[4] Univ Kasdi Merbah Ouargla, Lab Artificial Intelligence & Data Sci, PB 511, Ouargla 30000, Algeria
关键词
arial imagery; building detection; CNN; superpixels segmentation; VAE; NETWORK;
D O I
10.3390/buildings13071649
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Learning Sparse Geometric Features for Building Segmentation from Low-Resolution Remote-Sensing Images
    Liu, Zeping
    Tang, Hong
    REMOTE SENSING, 2023, 15 (07)
  • [42] SACNet: A Novel Self-Supervised Learning Method for Shadow Detection from High-Resolution Remote Sensing Images
    Chen, Dehai
    Kang, Jian
    Wang, Lanying
    Yu, Yongtao
    Zhou, Weixun
    Guan, Haiyan
    Karim, Mannan
    JOURNAL OF GEOVISUALIZATION AND SPATIAL ANALYSIS, 2025, 9 (01)
  • [43] Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
    Hu, Fan
    Xia, Gui-Song
    Hu, Jingwen
    Zhang, Liangpei
    REMOTE SENSING, 2015, 7 (11) : 14680 - 14707
  • [44] Segmentation based Building Detection in High Resolution Satellite Images
    Manandhar, Prajowal
    Aung, Zeyar
    Marpu, Prashanth Reddy
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 3783 - 3786
  • [45] Change Detection Based on Supervised Contrastive Learning for High-Resolution Remote Sensing Imagery
    Wang, Jue
    Zhong, Yanfei
    Zhang, Liangpei
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61
  • [46] Fast and accurate land cover classification on medium resolution remote sensing images using segmentation models
    Zhang, Wei
    Tang, Ping
    Zhao, Lijun
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2021, 42 (09) : 3277 - 3301
  • [47] Classification of cloud images by using super resolution, semantic segmentation approaches and binary sailfish optimization method with deep learning model
    Togacar, Mesut
    Ergen, Burhan
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 193
  • [48] Transformer-Driven Semantic Relation Inference for Multilabel Classification of High-Resolution Remote Sensing Images
    Tan, Xiaowei
    Xiao, Zhifeng
    Zhu, Jianjun
    Wan, Qiao
    Wang, Kai
    Li, Deren
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 : 1884 - 1901
  • [49] Deep learning-based semantic segmentation of remote sensing images: a review
    Lv, Jinna
    Shen, Qi
    Lv, Mingzheng
    Li, Yiran
    Shi, Lei
    Zhang, Peiying
    FRONTIERS IN ECOLOGY AND EVOLUTION, 2023, 11
  • [50] DB-BlendMask: Decomposed Attention and Balanced BlendMask for Instance Segmentation of High-Resolution Remote Sensing Images
    Chen, Zhenqian
    Shang, Yongheng
    Python, Andre
    Cai, Yuxiang
    Yin, Jianwei
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60