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 …
Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no
control over tumor growth. Deep learning has been argued to have the potential to …
control over tumor growth. Deep learning has been argued to have the potential to …
ICIF-Net: Intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection
Change detection (CD) of remote sensing (RS) images has enjoyed remarkable success by
virtue of convolutional neural networks (CNNs) with promising discriminative capabilities …
virtue of convolutional neural networks (CNNs) with promising discriminative capabilities …
Asymmetric cross-attention hierarchical network based on CNN and transformer for bitemporal remote sensing images change detection
X Zhang, S Cheng, L Wang, H Li - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
As an important task in the field of remote sensing (RS) image processing, RS image
change detection (CD) has made significant advances through the use of convolutional …
change detection (CD) has made significant advances through the use of convolutional …
Artificial intelligence image recognition method based on convolutional neural network algorithm
Y Tian - Ieee Access, 2020 - ieeexplore.ieee.org
As an algorithm with excellent performance, convolutional neural network has been widely
used in the field of image processing and achieved good results by relying on its own local …
used in the field of image processing and achieved good results by relying on its own local …
Granulated RCNN and multi-class deep sort for multi-object detection and tracking
In this article, two new models, namely granulated RCNN (G-RCNN) and multi-class deep
SORT (MCD-SORT), for object detection and tracking, respectively from videos are …
SORT (MCD-SORT), for object detection and tracking, respectively from videos are …
Brain tumor detection using deep neural network and machine learning algorithm
M Siar, M Teshnehlab - 2019 9th international conference on …, 2019 - ieeexplore.ieee.org
Brain tumor can be classified into two types: benign and malignant. Timely and prompt
disease detection and treatment plan leads to improved quality of life and increased life …
disease detection and treatment plan leads to improved quality of life and increased life …
FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features
The active recognition of interesting targets has been a vital issue for synthetic aperture
radar (SAR) systems. The SAR recognition methods are mainly grouped as follows …
radar (SAR) systems. The SAR recognition methods are mainly grouped as follows …
Analysis on change detection techniques for remote sensing applications: A review
Satellite images taken on the earth's surface are analyzed to identify the spatial and
temporal changes that have occurred naturally or manmade. Real-time prediction of change …
temporal changes that have occurred naturally or manmade. Real-time prediction of change …