Hyperspectral Image Processing: Anomaly Detection and Classification

In the study of earth science and remote sensing (RS), hyperspectral images (HSIs) have received increasing attention in recent years. Compared with other types of RS images, HSIs are composed of hundreds of continuous spectral bands and contain a large amount of spatial–spectral information, which can be used to distinguish targets of different materials at the pixel level. Reliable analysis results can be applied to many remote sensing scenarios, such as agricultural management, ecological observation, etc. To date, scholars have developed a large number of methods to classify pixels in HSI into different semantics. Initially, most of them were proposed based on traditional machine learning, such as decision trees, random forests, support vector machines, etc. With the development of deep learning, many deep networks have also been proposed, such as convolutional neural networks, recurrent neural networks, etc. However, as the content of HSIs becomes richer and the application requires more and more subdivided scenes, advanced technologies still need to be explored to fully mine the effective information of HSIs.

This Special Issue encourages the submission of papers on advanced machine/deep learning and image processing techniques for hyperspectral images.

• Hyperspectral anomaly detection;
• Hyperspectral image denoising;
• Hyperspectral image super-resolution;
• Hyperspectral target detection;
• Hyperspectral image fusion;
• Hyperspectral image classification.

Prof. Dr. Licheng Jiao
Prof. Dr. Xiangrong Zhang
Dr. Xu Tang
Guest Editors

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