Haze degrades optical data and reduces the accuracy of data interpretation. Haze detection and removal is a challenging and important task for optical multispectral data correction. This paper presents an empirical and automatic method for inhomogeneous haze detection and removal in medium-and high-resolution satellite optical multispectral images. The dark-object subtraction method is further developed to calculate a haze thickness map, allowing a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data. Rare scenes with a uniform and highly reflecting landcover result in limitations of the method. Evaluation on hazy multispectral data (Landsat 8 OLI and WorldView-2) and a comparison to haze-free reference data illustrate the spectral consistency after haze removal.

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The application potential of remotely sensed optical imagery is boosted through the increase in spatial resolution, and new analysis, interpretation, classification, and change detection methods are developed. Together with all the advantages, shadows are more present in such images, particularly in urban areas. This may lead to errors during data processing. The task of automatic shadow detection is still a current research topic. Since image acquisition is influenced by many factors such as sensor type, sun elevation and acquisition time, geographical coordinates of the scene, conditions and contents of the atmosphere, etc., the acquired imagery has highly varying intensity and spectral characteristics. The variance of these characteristics often leads to errors, using standard shadow detection methods. Moreover, for some scenes, these methods are inapplicable. In this paper, we present an alternative robust method for shadow detection. The method is based on the physical properties of a blackbody radiator. Instead of static methods, this method adaptively calculates the parameters for a particular scene and allows one to work with many different sensors and images obtained with different illumination conditions. Experimental assessment illustrates significant improvement for shadow detection on typical multispectral sensors in comparison to other shadow detection methods. Examples, as well as quantitative assessment of the results, are presented for Landsat-7 Enhanced Thematic Mapper Plus, IKONOS, World-View-2, and the German Aerospace Center (DLR) 3K Camera airborne system.

As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for a well-known test area. Focus is set on the influence of the expected radiometric calibration stability and the spectral calibration stability. Using a Monte-Carlo approach for uncertainty analysis, a larger number of realisations for the radiometric and spectral calibration are generated. Next, the ATCOR atmospheric correction is conducted for the test scene for each realisation. The subsequent analysis of the generated ground reflectance products is carried out independently for the radiometric and the spectral case. Findings are that the uncertainty in the L2A product is wavelength-dependent, and, due to the coupling with the estimation of atmospheric parameters, also spatially variable over the scene. To further illustrate the impact on subsequent data analysis, the influence on two vegetation indices is briefly analysed. Results show that the radiometric and spectral stability both have a high impact on the uncertainty of the narrow-band Photochemical Reflectance Index (PRI), and also the broad-band Normalized Difference Vegetation Index (NDVI) is affected.

Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency. Generally, the quantitative measures for pan-sharpening assessment were taken from other topics of image processing (e.g. image similarity indexes). All these measures are widely employed for this task but the applicability basis of these measures is not checked and proven. In this paper a comparison of pan-sharpening assessment measures for remote sensing is carried out on specially generated pan-sharpened images. Performed statistical analysis on the assessment measures allows to select the measures which are most sensitive to the pan-sharpened imagery quality and these measures are recommended for use.