Research progress on few-shot learning for remote sensing image interpretation
The rapid development of deep learning brings effective solutions for remote sensing image
interpretation. Training deep neural network models usually require a large number of …
interpretation. Training deep neural network models usually require a large number of …
A review of landcover classification with very-high resolution remotely sensed optical images—Analysis unit, model scalability and transferability
As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
Stagewise unsupervised domain adaptation with adversarial self-training for road segmentation of remote-sensing images
Road segmentation from remote-sensing images is a challenging task with wide ranges of
application potentials. Deep neural networks have advanced this field by leveraging the …
application potentials. Deep neural networks have advanced this field by leveraging the …
Domain adaptation in remote sensing image classification: A survey
Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …
samples for model training. When labeled samples are unavailable or labeled samples have …
Avoiding negative transfer for semantic segmentation of remote sensing images
Reducing the feature distribution shift caused by the factor of visual-environment changes,
named visual-environment changes (VE-changes), is a hot issue in domain adaptation …
named visual-environment changes (VE-changes), is a hot issue in domain adaptation …
[图书][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
[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] A review and meta-analysis of generative adversarial networks and their applications in remote sensing
Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Generative adversarial minority oversampling for spectral–spatial hyperspectral image classification
Recently, convolutional neural networks (CNNs) have exhibited commendable performance
for hyperspectral image (HSI) classification. Generally, an important number of samples are …
for hyperspectral image (HSI) classification. Generally, an important number of samples are …
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 …