Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
Deep learning-based change detection in remote sensing images: A review
Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …
development of remote sensing (RS) technology. These images significantly enhance the …
Change detection based on artificial intelligence: State-of-the-art and challenges
Change detection based on remote sensing (RS) data is an important method of detecting
changes on the Earth's surface and has a wide range of applications in urban planning …
changes on the Earth's surface and has a wide range of applications in urban planning …
Classification of hyperspectral image based on double-branch dual-attention mechanism network
In recent years, researchers have paid increasing attention on hyperspectral image (HSI)
classification using deep learning methods. To improve the accuracy and reduce the training …
classification using deep learning methods. To improve the accuracy and reduce the training …
Unsupervised deep change vector analysis for multiple-change detection in VHR images
Change detection (CD) in multitemporal images is an important application of remote
sensing. Recent technological evolution provided very high spatial resolution (VHR) …
sensing. Recent technological evolution provided very high spatial resolution (VHR) …
A feature difference convolutional neural network-based change detection method
Change detection based on remote sensing (RS) images has a wide range of applications
in many fields. However, many existing approaches for detecting changes in RS images with …
in many fields. However, many existing approaches for detecting changes in RS images with …
Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing
K Xu, H Huang, P Deng, Y Li - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
Scene classification of high spatial resolution (HSR) images can provide data support for
many practical applications, such as land planning and utilization, and it has been a crucial …
many practical applications, such as land planning and utilization, and it has been a crucial …
Machine learning in computer vision: a review
INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability
to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is …
to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is …
A survey on cooperative co-evolutionary algorithms
The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong
in 1994 and since then many CCEAs have been proposed and successfully applied to …
in 1994 and since then many CCEAs have been proposed and successfully applied to …
Vision transformer: An excellent teacher for guiding small networks in remote sensing image scene classification
K Xu, P Deng, H Huang - IEEE Transactions on Geoscience …, 2022 - ieeexplore.ieee.org
Scene classification is an active research topic in the remote sensing community, and
complex spatial layouts with various types of objects bring huge challenges to classification …
complex spatial layouts with various types of objects bring huge challenges to classification …