Advances in Remote Sensing Image Enhancement and Classification

Dear Colleagues,

Remote sensing technology plays a crucial role in acquiring information about the Earth's surface using sensors mounted on satellites or aircrafts. The acquired images often require enhancement and classification techniques to extract meaningful information. This summary explores recent advancements in remote sensing image enhancement and classification, focusing on two main streams: enhancement and classification.

Remote sensing image acquisition processes are generally influenced by various kinds of degradation, such as noise, geometric distortions, blur (motion, atmospheric turbulence, out-of-focus), etc. Image enhancement is becoming one of the central issues in the development of remote sensing. The enhancement explores fusing, denoising, and hardware design to improve the quality of images, enabling the extraction of more comprehensive and accurate knowledge.

Turning to image classification, researchers have explored various techniques to extract meaningful information from enhanced remote sensing images. Classification explores various research lines utilizing advanced algorithms and machine learning techniques to accurately classify remote sensing data.

This Special Issue aims to develop state-of-the-art technologies for remote sensing image enhancement and classification.

Furthermore, with the help of these new data and technologies, the application of remote sensing data can also be improved and expanded. Authors are sincerely invited to contribute their research results, focusing on cutting-edge technologies, novel applications, and remote sensing classification and improvement evaluation methods, including, but not limited to, the following topics:

For enhancement:

1. Fusion-based enhancement (multi-modal, multi-temporal, multi-source, multi-sensor, etc.);
2. Super-resolution-based enhancement;
3. Denoising-based enhancement;
4. Quality assessment for enhanced images;
5. Design of imaging sensor/system.

For classification, there are even more research lines, such as:

1. Hyperspectral image classification;
2. New image classification architectures;
3. New datasets for remote sensing image classification with deep learning;
4. Remote sensing image processing and pattern recognition;
5. Scene classification;
6. Image or data fusion/fusion classification;
7. Target detection/change detection.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Penelope Wang via <[email protected]> directly.

Dr. Xu Li
Guest Editor

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