A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.
Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.
School of Land Sciences and Technology, China University of Geosciences, Beijing, China ; Key Laboratory of Land Regulation, Ministry of Land and Resources, Beijing, China.
Tao
T
Yuan
School of Land Sciences and Technology, China University of Geosciences, Beijing, China ; Key Laboratory of Land Regulation, Ministry of Land and Resources, Beijing, China.
Xinqi
X
Zheng
School of Land Sciences and Technology, China University of Geosciences, Beijing, China ; Key Laboratory of Land Regulation, Ministry of Land and Resources, Beijing, China.
Xuan
X
Hu
School of Land Sciences and Technology, China University of Geosciences, Beijing, China ; Key Laboratory of Land Regulation, Ministry of Land and Resources, Beijing, China.
Wei
W
Zhou
School of Land Sciences and Technology, China University of Geosciences, Beijing, China.
Wei
W
Wang
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D061848:Optical Imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D058998:Remote Sensing Technology / Q000379:methods*; D013223:Statistics as Topic