Due to imaging platform and conditions constraints, multiple types of hybrid distortions exist in on-board reconnaissance images. Research on quality assessment of reconnaissance images can provide important quantitative basis and reference for performance optimization of subsequent processing and imaging system. By analyzing characteristics of reconnaissance images, 11 kinds of relevant features from 3 categories such as camera shake, structure changes, and color loss are extracted in conditions of multi-degree freedom and multi-attitude changes of imaging platform. Here we use high resolution mapping images as the original image set, and extract features of image patches. Benchmark distribution characteristics are obtained by multivariate Gaussian fitting. Using the learned multivariate Gaussian model, a Mahalanobis distance is used to measure the quality of each patch of on-board reconnaissance images, then overall quality score is obtained by average pooling. When tested images from real on-board imaging platform, the proposed method is shown to correlate highly with human judgments of quality and have superior quality-prediction performance to state-of-the-art blind image quality assessment methods.