Convolutional Neural Networks Application in Remote Sensing
A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "AI in Imaging".
Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 5592
Special Issue Editors
Interests: artificial intelligence; computer vision; image and video processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; computer vision; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing is the process of acquiring information and monitoring the physical characteristics of an area using special satellite and aircraft-based sensors. Remote sensing is crucial in a plethora of applications ranging from detecting land use and cover to observing climate and urban changes and from controlling forest fires to identifying crop production and damage. Remote sensing began in the 1960s and 1970s with the development of image processing of satellite imagery, but it greatly benefited from the advances in machine and deep learning. Today, convolutional neural networks can process remote sensing images with high speed and achieve impeccable accuracy and robustness in several applications. However, technological breakthroughs are still needed to enhance the performance of remote sensing applications and facilitate their use in real life.
This Special Issue of the Journal of Imaging aims to feature reports of recent advances in remote sensing technology, novel deep network architectures to enhance the accuracy and robustness of remote sensing applications, such as object segmentation and change detection, and innovative real-time applications that can be employed in real life to cover important needs in remote sensing.
Dr. Dimitrios Konstantinidis
Dr. Kosmas Dimitropoulos
Guest Editors
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Keywords
- remote sensing
- multispectral imaging
- deep networks
- convolutional neural networks
- computer vision
- object detection and segmentation
- change detection
- scene understanding
- attention-based networks
- image fusion
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