Performance Comparison of Deep Learning (DL)-Based Tabular Models for Building Mapping Using High-Resolution Red, Green, and Blue Imagery and the Geographic Object-Based Image Analysis Framework

被引:2
作者
Hossain, Mohammad D. [1 ]
Chen, Dongmei [1 ]
机构
[1] Queens Univ, Dept Geog & Planning, Lab Geog Informat & Spatial Anal, Kingston, ON K7L 3N6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
building extraction; GEOBIA; deep learning; tabular model; SVM; RF; XGB; UNMANNED AERIAL VEHICLE; SATELLITE IMAGES; RANDOM FOREST; EXTRACTION; CLASSIFICATION; SEGMENTATION; PLANTS;
D O I
10.3390/rs16050878
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction.
引用
收藏
页数:26
相关论文
共 62 条
  • [1] Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours
    Ahmadi, Salman
    Zoej, M. J. Valadan
    Ebadi, Hamid
    Moghaddam, Hamid Abrishami
    Mohammadzadeh, Ali
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2010, 12 (03): : 150 - 157
  • [2] Arik SO, 2021, AAAI CONF ARTIF INTE, V35, P6679
  • [3] Random forest in remote sensing: A review of applications and future directions
    Belgiu, Mariana
    Dragut, Lucian
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 114 : 24 - 31
  • [4] Benarchid O., 2013, Canadian Journal on Image Processing and Computer Vision, V4, P1
  • [5] Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an on board digital photographic camera
    Beniaich, Adnane
    Naves Silva, Marx Leandro
    Pomar Avalos, Fabio Arnaldo
    de Menezes, Michele Duarte
    Candido, Bernardo Moreira
    [J]. SEMINA-CIENCIAS AGRARIAS, 2019, 40 (01): : 49 - 65
  • [6] Geographic Object-Based Image Analysis - Towards a new paradigm
    Blaschke, Thomas
    Hay, Geoffrey J.
    Kelly, Maggi
    Lang, Stefan
    Hofmann, Peter
    Addink, Elisabeth
    Feitosa, Raul Queiroz
    van der Meer, Freek
    van der Werff, Harald
    van Coillie, Frieke
    Tiede, Dirk
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 87 : 180 - 191
  • [7] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [8] Gradient Boosting Machine and Object-Based CNN for Land Cover Classification
    Bui, Quang-Thanh
    Chou, Tien-Yin
    Hoang, Thanh-Van
    Fang, Yao-Min
    Mu, Ching-Yun
    Huang, Pi-Hui
    Pham, Vu-Dong
    Nguyen, Quoc-Huy
    Do Thi Ngoc Anh
    Pham, Van-Manh
    Meadows, Michael E.
    [J]. REMOTE SENSING, 2021, 13 (14)
  • [9] A Probabilistic Framework for Building Extraction From Airborne Color Image and DSM
    Chai, Dengfeng
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2017, 10 (03) : 948 - 959
  • [10] Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
    Chan, Jonathan Cheung-Wai
    Paelinckx, Desire
    [J]. REMOTE SENSING OF ENVIRONMENT, 2008, 112 (06) : 2999 - 3011