Assisted learning for land use classification: The important role of semantic correlation between heterogeneous images-Reference-Cited by-同舟云学术

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Assisted learning for land use classification: The important role of semantic correlation between heterogeneous images

Published:2024-02 Issue: Volume:208 Page:158-175
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Li Wangbin,Sun Kaimin,Li Wenzhuo^ORCID,Huang Xiao,Wei Jinjiang,Chen Yepei^ORCID,Cui Wei,Chen Xueyu,Lv Xianwei

Publisher

Elsevier BV

Reference69 articles.

1. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using sentinel-2 data;Abdi;GISci. Remote Sens.,2020

2. Sentinel SAR-optical fusion for crop type mapping using deep learning and google earth engine;Adrian;ISPRS J. Photogramm. Remote Sens.,2021

3. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks;Audebert,2017

4. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks;Audebert;ISPRS J. Photogram. Remote Sens.,2018

5. Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability;Avand;J. Hydrol.,2021

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