Similarity-Based Neural Network Model for FBIR System ofOptical Satellite Image Quality Assessment
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Satellite images are an important tool of Earth observation, as well as for observing man-made and natural resources. Multiple satellites are in space, providing numerous images on a daily/weekly basis for land surface interpretation, study, and various type of monitoring. Satellite images are a significant source of evidence and information used in a variety of areas, including environmental impact measurement, agricultural tracking, woodland survey, and identification of changes in urban areas. It is still a difficult task to retrieve a noise-free image from an image repository and extract meaningful information from an image, especially optical satellite images that may be weather effected, which are less capable to generate efficient and accurate results for earth surface monitoring particularly when the image contains multiple semantic information. Many researchers have developed different neural network models for similarity measurement that are capable of finding similarity between two patterns in the literature, but finding similarity between two satellite images is a difficult challenge since satellite images relate to normal datasets. In this paper, a similarity-based neural network (SBNN) model for the FBIR system has been developed. The SBNN model is a replacement forthe KNN algorithm, which was proposed in our previous study. The SBNN model calculates the image band similarity between the required image and the referenced image, and based on band similarity, the system predicts that the image is suitable for post-processing or not. The improved version of the FBIR system (i.e. FBIR with SBNN model) allows the retrievalof pre-processed band data of the image as per user requirement. The proposed FBIR system minimizes the downloading time, wastage of the internet, and the most important thing is the time of the user that is consumed during the pre-processing of raw images. The main advantage of SBNN based FBIR systemis that there will not be a need to download a complete image (i.e. composite image of all the respective bands), user can select the required individual band image and can easily download it through a user-friendly GUI. In future, SBNN based FBIR model can be very beneficent for the scientist and environmentalist to conduct quick case studies for urban heat mapping, precision agriculture, coal fire monitoring and forest fire mapping, etc..