Remote sensing image quality evaluation based on deep learning
| Autor: | Xue Yang, Hemeng Yang, Ruizeng Wei, Zhu Ling, Tong Wang, Enze Zhou, Fan Yazhou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology business.industry Computer science Image quality Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Engineering 02 engineering and technology 020901 industrial engineering & automation Artificial Intelligence Remote sensing (archaeology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Remote sensing |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. :1-9 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-219109 |
| Popis: | Remote sensing technology is an effective tool for sensing the earth’s surface. With the continuous improvement of remote sensing technology, remote sensing detectors can obtain more spectral and spatial information, including clear feature contours, complex texture features and spatial layout rules. This information was detected in mineral resources. Surface substance identification, water pollution information monitoring and many other aspects have played an important role. The coding algorithm and defects, storage algorithm and interference from atmospheric cloud radiation information during the imaging process lead to varying degrees of distortion and deterioration of remote sensing images during imaging, transmission and storage. This makes it difficult to process, analyze and apply remote sensing images. Therefore, the design of a reasonable remote sensing image quality evaluation method is not only conducive to the remote sensing image quality evaluation in the real-time processing system of remote sensing image, but also conducive to the optimization of remote sensing image system and image processing algorithm. The application is worthwhile. In this paper, the deteriorating features of remote sensing images will change the statistical distribution. We propose a method for evaluating the quality of remote sensing images in depth learning. Feature learning and blurring as well as noise intensity classification for image remote sensing using convolutional neural network are carried out. The evaluation model is modified by masking effect and perceptual weighting factor, and the quality evaluation results of remote sensing images are obtained according to human vision. The research shows that this method can effectively solve the problem of removing and evaluating the noise of remote sensing image, and can effectively and accurately evaluate the quality of remote sensing image. It is also consistent with subjective assessment and human perception. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|