The Quality Evaluation Method Research on Cloud Cover Image Based on Statistical Analysis

Cloud cover is a key factor which affects the quality of aerial and remote sensing image. In order to precisely evaluate the impact which brought by cloud cover, article constructs a cloud detection algorithm which suits for aerial image according to cloud statistical characters; analyzing the cloud region, and building a model on the basis of the image quality comprehensive assessment of the thick cloud, thin cloud, possible cloud and cloudless region; at last, utilizing experts’ subjective guidance to improve the evaluation model. The algorithm can efficiently and precisely evaluate the aerial cloud cover image on the account massive test data. Introduction Image is an important way for human to acquire information. Therefore, constructing image quality evaluation mechanism is significant[1][2]. Along with development of aviation technique and simulation, aerial image application is used more and more widely. However, due to many impact factors such as height and climate, it’s hard to get the image without any cloud cover. Cloud cover has become main impact factor of affecting aerial image quality. Evaluating cloud cover image is valuable for aviation image application and process tools. So far, the normal method of evaluating cloud cover image is that firstly detect cloud cover region and then evaluate the quality of this region[3]. Cloud cover region detection method can be divided into two types: first type is spectrum analysis method which using reflectance value which get by multi-channel data acting on cloud and land, and second is using cloud’s texture statistical feature; article takes no reference methods such as PSNR, SSIM, VIF[4][5] as criteria calculation. Because of the roughness of the detection result, simplicity of the model, article got a low accuracy of evaluation result and not well performed. For the above application requirements, article combines statistic methods with meticulous classification for the cloud region and using learning theories to improve the evaluation model and construct more accurate objective evaluation model. Feature analysis In the field of cloud detection, useful detection feature include: grayscale, shape, texture, shadow area six types features. Different types of clouds have different feature value because of height, temperature, climate etc. Therefore, we use mathematic statistic methods to do multi-feature evaluation at cloud region in order to get more accurate detection result. In the field of image evaluation, there are two general types: subjective method and objective method. The former method relies on humans’ subjective perception to evaluate image; the latter method evaluate image according to quantitative criteria, for instance: PSNR, SSIM, VIF etc. Subjective quality evaluation is the most reliable method but restricted by human power, time cost, poor generalization[7]. Along with the development of AI, we can use the result of subjective evaluation to guide the objective model to get a better evaluation method. Article utilizes angle second order matrix and fractal dimension to detect and classify the cloud region as well as using experts’ evaluation result and neural network to build evaluation model, deal 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) © 2015. The authors Published by Atlantis Press 1028