MSBR-GNet: A High-Resolution Imagery Generative Optimization Model for Building Rooftop Boundary Guided by Interpretable Statistical Model in Spatial and Spectral Domain-Reference-Cited by-同舟云学术

MSBR-GNet: A High-Resolution Imagery Generative Optimization Model for Building Rooftop Boundary Guided by Interpretable Statistical Model in Spatial and Spectral Domain

Published:2024 Issue: Volume:17 Page:8164-8188
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Liu Jianhua¹^ORCID,Ning Xiaohe²^ORCID,Wang Mengchen²^ORCID,Wang Xinyu²^ORCID,Liu Yuan³^ORCID,Chen Xiaoyou²^ORCID,Zeng Shiyi²^ORCID

Affiliation:

1. School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, the Beijing Key Laboratory of Urban Spatial Information Engineering, the Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing, China

2. School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, China

3. Beijing Institute of Surveying and Mapping, Beijing, China

Funder

Beijing Key Laboratory of Urban Spatial Information Engineering

Beijing Institute of Surveying and Mapping Design

Beijing association of higher education

Beijing University of Civil Engineering and Architecture Education Scientific Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10483255.pdf?arnumber=10483255

Reference58 articles.

1. Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

2. Adaptive Algorithm for Automated Polygonal Approximation of High Spatial Resolution Remote Sensing Imagery Segmentation Contours

3. Machine-learned Regularization and Polygonization of Building Segmentation Masks

4. Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods;Lorenz,2021

5. Object-based morphological building index for building extraction from high resolution remote sensing imagery;Lin,2017