Nowadays, there is an exponentially increasing amount of data from cameras, webcams, or other optical or radar sensors. Proper ways of mining and using this data could have significant applications across a variety of sectors and industries. For example, optical and radar images can be employed to detect and recognize intended targets within large scenes to help intelligence interpretation and battlefield surveillance. Furthermore, the data from different types of sensors can be properly fused to find more latent information.

However, there are still many challenges in the development of automatic and intelligent algorithms to process massive data from different sensors. Recently, advances in machine learning have shown great potential in practical applications, including signal processing, image interpretation, and data fusion. In particular, deep learning algorithms like convolutional neural networks (CNNs), long short-term memory (LSTM), or generative adversarial networks (GANs), provide powerful tools for processing different sources of data, including both low-dimensional and high-dimensional. With the help of advanced machine learning algorithms, high volumes of data can be processed and analyzed with high efficiency and precision. Therefore, the proper employment of machine learning techniques in the field of data processing from multiple sensors is vital.

The aim of this Special Issue is to apply advanced machine learning approaches in processing data from multiple sensors. We hope to provide novel guidance for machine learning researchers and broaden the perspectives of machine learning and sensor-related researchers. Original research and review articles on both theoretical and application-oriented works are welcome.

Potential topics include but are not limited to the following:

• Machine learning in optical image processing
• Machine learning in video processing
• Machine learning in radar signal/image processing
• Machine learning in system design
• Machine learning in the Internet of Things
• Machine learning in multi-sensor data fusion
• Machine learning in the cooperative working of multiple sensors
• Deep learning for signal representation and processing
• Deep learning for multi-sensor processing
• Deep learning for sensor-related applications