Application of Prior Knowledge-Driven Neural Networks for Remote Sensing Image Processing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 1841
Special Issue Editor
Interests: tensor analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning has developed rapidly in recent years. With the great improvement of computing power, many ideas of deep learning have been realized. However, deep learning has a disadvantage: it requires a large number of samples to train in order to achieve better generalization. But for many applications, it is not easy to obtain sufficient and high-quality data to model such problems. Furthermore, because many of the models we have established lack common sense, they do not have the knowledge of the human world, so they are easily attacked. Compared with machines, humans rely on prior knowledge of the target (such as visual information, context layout, etc.), and can easily and efficiently find the target object in an unknown dynamic environment. In order to solve these problems, additional prior knowledge is incorporated into the training process of the model. Prior knowledge plays a role in guiding and constraining the learning process of the model. By incorporating prior knowledge into the model, we can learn and understand the data more effectively and improve the performance and generalization ability of the model. The aim of this topic is to provide high-quality, up-to-date approaches to process remote sensing images. The key point is to develop efficient remote sensing image processing technology based on prior knowledge-driven neural networks. Articles may address, but are not limited to, the following topics:
- Deep learning based on prior knowledge-driven neural networks;
- Image classification based on prior knowledge-driven neural networks;
- Target detection based on prior knowledge-driven neural networks;
- Prior knowledge-driven neural networks for environmental monitoring;
- Prior knowledge-driven deep learning networks for image recovery;
- Intelligent navigation based on prior knowledge-driven neural networks;
- Remote sensing image registration based on prior knowledge-driven neural networks;
- Intelligent image segmentation based on prior knowledge-driven neural networks;
- Intelligent target positioning and precise guidance.
Prof. Dr. Hu Zhu
Guest Editor
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Keywords
- remote sensing image processing
- deep learning
- image classification
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