Airborne hyperspectral images used for remote sensing are distorted by various factors, such as atmospheric effects, transmission noise, instrumentation noise, and motion blurring. Proper assessment of image quality is extremely important in the identification and characterization of distortion, evaluation of compression performance, and so on. We present an ensemble feature-based full-referenced approach to quantify the quality of remotely sensed hyperspectral images. Our ensemble features quantify the objective quality of the image inconsistency with the visual measure and identify the inherent distortions. The proposed approach identifies the distinct spatial structural image features from the images corresponding to each spectral band and obtains the hyperspectral cube quality by computing the mean. The measure also identifies the highly distorted spectral bands, which must be restored or eliminated before processing. We evaluate objective image quality in several real hyperspectral images and conclude that our proposed approach evaluates the image quality more efficiently compared to the existing approaches.