A comprehensive review on deep learning based remote sensing image super-resolution methods
Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …
Earth Science field. However, due to the limitation of the optic and sensor technologies and …
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …
management, environmental modelling and assessment, and agricultural production …
An overview of the applications of earth observation satellite data: impacts and future trends
As satellite observation technology develops and the number of Earth observation (EO)
satellites increases, satellite observations have become essential to developments in the …
satellites increases, satellite observations have become essential to developments in the …
A review of deep learning in multiscale agricultural sensing
Population growth, climate change, and the worldwide COVID-19 pandemic are imposing
increasing pressure on global agricultural production. The challenge of increasing crop yield …
increasing pressure on global agricultural production. The challenge of increasing crop yield …
Perceiving spectral variation: Unsupervised spectrum motion feature learning for hyperspectral image classification
Y Sun, B Liu, X Yu, A Yu, K Gao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …
achieved significant development. The superior capability of feature extraction from these …
Remote sensing image classification: A comprehensive review and applications
Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate
construction materials and provide detailed geographic information. In remote sensing …
construction materials and provide detailed geographic information. In remote sensing …
Mutual attention inception network for remote sensing visual question answering
Remote sensing images (RSIs) containing various ground objects have been applied in
many fields. To make semantic understanding of RSIs objective and interactive, the task …
many fields. To make semantic understanding of RSIs objective and interactive, the task …
A systematic review on advancements in remote sensing for assessing and monitoring land use and land cover changes impacts on surface water resources in semi …
This study aimed to provide a systematic overview of the progress made in utilizing remote
sensing for assessing the impacts of land use and land cover (LULC) changes on water …
sensing for assessing the impacts of land use and land cover (LULC) changes on water …
Hyperspectral image classification using attention-based bidirectional long short-term memory network
Deep neural networks have been widely applied to hyperspectral image (HSI) classification
areas, in which recurrent neural network (RNN) is one of the most typical networks. Most of …
areas, in which recurrent neural network (RNN) is one of the most typical networks. Most of …
Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions
MA Moharram, DM Sundaram - Neurocomputing, 2023 - Elsevier
Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …