[HTML][HTML] Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation
M Luo, S Ji - ISPRS Journal of Photogrammetry and Remote …, 2022 - Elsevier
Automatic land use/land cover (LULC) classification from very high resolution (VHR) remote
sensing images can provide us with rapid, large-scale, and fine-grained understanding of …
sensing images can provide us with rapid, large-scale, and fine-grained understanding of …
[HTML][HTML] Enabling country-scale land cover mapping with meter-resolution satellite imagery
High-resolution satellite images can provide abundant, detailed spatial information for land
cover classification, which is particularly important for studying the complicated built …
cover classification, which is particularly important for studying the complicated built …
Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery
Urban land-cover information is essential for resource allocation and sustainable urban
development. Recently, deep learning algorithms have shown promising results in land …
development. Recently, deep learning algorithms have shown promising results in land …
A weakly pseudo-supervised decorrelated subdomain adaptation framework for cross-domain land-use classification
High spatial resolution (HSR) remote-sensing image scene classification is a crucial way for
land-use interpretation. However, most of the current scene classification methods assume …
land-use interpretation. However, most of the current scene classification methods assume …
Land-cover classification with high-resolution remote sensing images using transferable deep models
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are
available for land-cover mapping. However, due to the complex information brought by the …
available for land-cover mapping. However, due to the complex information brought by the …
[PDF][PDF] Learning transferable deep models for land-use classification with high-resolution remote sensing images
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are
available for land-use mapping. However, due to the complex information brought by the …
available for land-use mapping. However, due to the complex information brought by the …
Deep transfer learning for land use and land cover classification: A comparative study
R Naushad, T Kaur, E Ghaderpour - Sensors, 2021 - mdpi.com
Efficiently implementing remote sensing image classification with high spatial resolution
imagery can provide significant value in land use and land cover (LULC) classification. The …
imagery can provide significant value in land use and land cover (LULC) classification. The …
Attention-based dynamic alignment and dynamic distribution adaptation for remote sensing cross-domain scene classification
Due to the lack of high-quality labeled data and poor generalization ability of supervised
models in remote sensing scene classification, cross-domain scene classification is …
models in remote sensing scene classification, cross-domain scene classification is …
Generative adversarial network-based full-space domain adaptation for land cover classification from multiple-source remote sensing images
S Ji, D Wang, M Luo - IEEE Transactions on Geoscience and …, 2020 - ieeexplore.ieee.org
The accuracy of remote sensing image segmentation and classification is known to
dramatically decrease when the source and target images are from different sources; while …
dramatically decrease when the source and target images are from different sources; while …
Joint Deep Learning for land cover and land use classification
Land cover (LC) and land use (LU) have commonly been classified separately from remotely
sensed imagery, without considering the intrinsically hierarchical and nested relationships …
sensed imagery, without considering the intrinsically hierarchical and nested relationships …