Application of Multi-Sensor Image Fusion of Internet of Things in Image Processing

• Main Authors: Hong Li, Shuying Liu, Qun Duan, Weibin Li
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Internet of Things; multisensor; image fusion; homogeneous region; adaptive gain
• Online Access: https://ieeexplore.ieee.org/document/8452895/

The perception layer of Internet of Things (IOT) consists of various sensors. It is the source of the IOT to identify objects and collect information. Information fusion collected from multi-sensor has been widely used in various fields, such as intelligent industry, intelligent agriculture, intelligent transportation, and intelligent environmental protection. In this paper, multi-sensor image fusion, multispectral (MS) and panchromatic (PAN) images, is studied, and the fused images are used in target detection, recognition, and classification. However, traditional methods based on an injection model generally consider the MS images as a whole to compute the spectral weights. They ignore the local information of MS images and produce some spectral distortions, because for different objects, the spectral response will be different. Therefore, we propose a novel multi-sensor image fusion based on application layer of IOT (IFIOT) to preserve the spectral information of MS images. In this method, local homogeneous areas are found first by superpixel segmentation. Due to good properties of superpixel, the homogeneous areas are uniform and contain only one kind of object. Then, we estimate the spectral weights for different bands on the homogeneous area. The injection gain has an important influence on fusion results. Therefore, we adaptively compute the gain coefficients by minimizing the error between the spectral degraded MS and PAN images. Finally, after the injection of spatial details obtaining from the PAN image, fused images are produced. Experimental results reveal that the IFIOT method can give good fusion results and the spectral information is preserved well.