Abstract
More and more high-spatial resolution satellite images are produced with the improvement of satellite technology. However, the quality of images is not always satisfactory for application. Due to the impact of complicated atmospheric conditions and complex radiation transmission process in imaging process the images often suffer deterioration. In order to assess the quality of remote sensing images over urban areas, we proposed a general purpose image quality assessment methods based on feature extraction and machine learning. We use two types of features in multi scales. One is from the shape of histogram the other is from the natural scene statistics based on Generalized Gaussian distribution (GGD). A 20-D feature vector for each scale is extracted and is assumed to capture the RS image quality degradation characteristics. We use SVM to learn to predict image quality scores from these features. In order to do the evaluation, we construct a median scale dataset for training and testing with subjects taking part in to give the human opinions of degraded images. We use ZY3 satellite images over Wuhan area (a city in China) to conduct experiments. Experimental results show the correlation of the predicted scores and the subjective perceptions.
Highlights
Chinese high resolution remote sensing satellites have generated a large number of remote sensing (RS) images every day
It has been generally noticed that the histogram indicates the probability distribution of image gray level and when computed over multi scales it reveals the statistical features in scale space
The dataset was derived from a set of source RS images acquired by a multispectral camera loaded on ZY3 satellite
Summary
Chinese high resolution remote sensing satellites have generated a large number of remote sensing (RS) images every day. From the point of view of information theory and signal processing the above features try to capture the image edges and details, simulate the image blurring and contrast degradation and provide powerful tools to assess the image quality. Some recent research includes CNN method (Kang, 2014), deep learning network (Gu, 2014), DOG model and random forest (Pei, 2015) These methods try to reveal the image quality degradation mechanism in feature space by training an empirical model the prediction model is used to calculate the test image quality score. The first contribution of this work is the development of a modular framework for image quality assessment of cloud and mist-deteriorated multi-spectral RS urban images. We demonstrate that our algorithm performs well in terms of correlation with human perception
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