An object-based and heterogeneous segment filter convolutional neural network for high-resolution remote sensing image classiﬁcation-Reference-Cited by-同舟云学术

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An object-based and heterogeneous segment filter convolutional neural network for high-resolution remote sensing image classiﬁcation

Published:2019-02-27 Issue:15 Volume:40 Page:5892-5916
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Pan Xin¹²,Zhao Jian¹,Xu Jun²

Affiliation:

1. School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, China

2. The Key Laboratory of Changbai Mountain Historical Culture and VR Technology Reconfiguration, Changchun Institute of Technology, Changchun, China

Funder

National Natural Science Foundation of China

Foundation of Jilin Provincial Science & Technology Department

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2019.1584687

Reference40 articles.

1. Building detection using directional spatial constraints

2. Object based image analysis for remote sensing

3. Geographic Object-Based Image Analysis – Towards a new paradigm

4. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

5. A GEOBIA Methodology for Fragmented Agricultural Landscapes

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1. Target-based building extraction from high-resolution RGB imagery using GEOBIA framework and tabular deep learning model;Geomatica;2024-07

2. Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification;Remote Sensing;2024-04-18

3. U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices;Drones;2023-11-09

4. Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning;Polish Journal of Environmental Studies;2023-02-27

5. Coupling of deep learning and remote sensing: a comprehensive systematic literature review;International Journal of Remote Sensing;2023-01-02

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