[HTML][HTML] A review on deep learning in UAV remote sensing
Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …
capability, and brought important breakthroughs for processing images, time-series, natural …
The eyes of the gods: A survey of unsupervised domain adaptation methods based on remote sensing data
With the rapid development of the remote sensing monitoring and computer vision
technology, the deep learning method has made a great progress to achieve applications …
technology, the deep learning method has made a great progress to achieve applications …
Unsupervised domain adaptation for semantic segmentation of high-resolution remote sensing imagery driven by category-certainty attention
J Chen, J Zhu, Y Guo, G Sun, Y Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Semantic segmentation is an important task of analysis and understanding of high-
resolution remote sensing images (HRSIs). The deep convolutional neural network (DCNN) …
resolution remote sensing images (HRSIs). The deep convolutional neural network (DCNN) …
Transfer learning models for land cover and land use classification in remote sensing image
ABSTRACT Land Cover or Land Use (LCLU) classification is an important, challenging
problem in remote sensing (RS) images. RS image classification is a recent technology …
problem in remote sensing (RS) images. RS image classification is a recent technology …
SPGAN-DA: Semantic-preserved generative adversarial network for domain adaptive remote sensing image semantic segmentation
Unsupervised domain adaptation for remote sensing semantic segmentation seeks to adapt
a model trained on the labeled source domain to the unlabeled target domain. One of the …
a model trained on the labeled source domain to the unlabeled target domain. One of the …
Prototype and context-enhanced learning for unsupervised domain adaptation semantic segmentation of remote sensing images
K Gao, A Yu, X You, C Qiu, B Liu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In unsupervised domain adaptation (UDA) of remote sensing images (RSIs), the huge
interdomain discrepancies and intradomain variances lead to complicated class-level …
interdomain discrepancies and intradomain variances lead to complicated class-level …
Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions
Crop type mapping at the field level is critical for a variety of applications in agricultural
monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input …
monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input …
Entropy guided adversarial domain adaptation for aerial image semantic segmentation
A Zheng, M Wang, C Li, J Tang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Recent advances on aerial image semantic segmentation mainly employ the domain
adaption to transfer knowledge from the source domain to the target domain. Despite the …
adaption to transfer knowledge from the source domain to the target domain. Despite the …
Data-efficient domain adaptation for semantic segmentation of aerial imagery using generative adversarial networks
Despite the significant advances noted in semantic segmentation of aerial imagery, a
considerable limitation is blocking its adoption in real cases. If we test a segmentation model …
considerable limitation is blocking its adoption in real cases. If we test a segmentation model …
[HTML][HTML] Identifying wetland areas in historical maps using deep convolutional neural networks
N Ståhl, L Weimann - Ecological Informatics, 2022 - Elsevier
The local environment and land usages have changed a lot during the past one hundred
years. Historical documents and materials are crucial in understanding and following these …
years. Historical documents and materials are crucial in understanding and following these …