# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 1 Introduction
In most cases, natural images are characterized by particular regular statistical properties that are quantifiably modified by the occurrence of distortions. The luminance parameters of natural images are generally locally normalized and conform to the Gaussian-like distribution. The no-reference image quality assessment is purely spatial and is dependent on a spatial NSS model that does not require co-ordinate domain transformation. The model is highly efficient and is valuable for optimizing image processing techniques such as denoising. Image quality assessment is usually constrained by the absence of reference images. [PERSON] [29] argues that the quality indicators generated by full reference models are not quality measures in the absolute sense given that these approaches assess fidelity relative to a reference image whose pristine nature is uncertain since all images are inherently prone to distortions. It is therefore argued that the performance of image quality assessment models should ideally be predicated on the correlation with the subjective judgements of quality. The BRISQUE algorithm is one of the most widely used blind reference methods to assess image quality. BRISQUE was used in the quality assessment of satellite stereo images by [PERSON] [31] and yielded favorable results compared to the Distortion Identification based Image Verity and INtegrity Assessment Evaluation (DIIVINE), BLind Image Integrity Notator using DCT Statistics-II (BLIINDS-II), Spatial-Spectral Entropy-based Quality (SSEQ), and Blind Image Quality Indices (BIQI). [PERSON] [32] modified the BRISQUE model to assess ZY3 satellite image degradation caused by atmospheric blurring, clouds, reflectance, sampling, and quantization in urban areas. The method is based on support vector machines and natural scene statistics model derived from the BRISQUE algorithm. The results revealed a correlation between predicted scores and human subjective perception. [PERSON] [33] evaluated the image scores generated by the BRISQUE model on GeoEye1, QuickBird2, WorldView2, and WorldView3 satellite images and noted a monotonic decrease with increasing image quality and focal length of the remote sensing system. The BRISQUE model also produced superior results when compared to the no-reference image assessment model based on spatial and spectral entropies developed by [PERSON] [34]. [PERSON] [35] used BRISQUE to assess the performance of image dehazing algorithm. In contrast to most blind image quality models, BRISQUE evades the characterization of distortion-specific features such as ringing, blur, or blocking by measuring the loss of naturalness in the image by computing scene statistics of locally normalized luminance values. The proliferation of nanosatellites with imaging payloads ushers in new opportunities for remote sensing. The satellite data provided by CubeSats have huge potential to benefit earth observation users involved in cartographic land use and land cover mapping applications and in bridging the data gaps in a time-sensitive application such as disaster management. One of the mission objectives for the nSight-1 CubeSat was to provide the first space flight heritage for the Gecko imager. While this mission objective is sufficient to ascertain the innovative engineering capability of the imager as a demonstrator mission, the mission objectives are silent of the primary earth observation applications the nSight-1 mission seeks to achieve and the expected data quality standards. The selection of the most appropriate image quality parameter to use is largely driven by remote sensing applications. Visual interpretation plays a critical role in cartographic applications and the most meaningful image quality measures are considered to be based on the visual assessments by human observers [26].