Aliaksei Makarau

Adaptive Shadow Detection Using a Blackbody Radiator Model

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, WorldView-2, and the German Aerospace Center (DLR) 3K Camera airborne system.

Haze Detection and Removal in Remotely Sensed Multispectral Imagery

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.

Analysis and selection of pan-sharpening assessment measures

Pan-sharpening of remote sensing multispectral imagery directly influences the accuracy of interpretation, classification, and other data mining methods. Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency, complexity of the pan-sharpening method, and other properties. The quality of a pan-sharpened image is assessed using quantitative measures. Generally, the quantitative measures for pan-sharpening assessment are taken from other topics of image processing (e.g., image similarity indexes), but the applicability basis of these measures (i.e., whether a measure provides correct and undistorted assessment of pan-sharpened imagery) is not checked and proven. For example, should (or should not) a quantitative measure be used for pan-sharpening assessment is still an open research topic. Also, there is a chance that some measures can provide distorted results of the quality assessment and the suitability of these quantitative measures as well as the application for pan-sharpened imagery assessment is under question. The aim of the authors is to perform statistical analysis of widely employed measures for remote sensing imagery pan-sharpening assessment and to show which of the measures are the most suitable for use. To find and prove which measures are the most suitable, sets of multispectral images are processed by the general fusion framework method (GFF) with varying parameters. The GFF is a type of general image fusion method. Variation of the method parameter set values allows one to produce imagery data with predefined quality (i.e., spatial and spectral consistency) for further statistical analysis of the assessment measures. The use of several main multispectral sensors (Landsat 7 ETM + , IKONOS, and WorldView-2) imagery allows one to assess and compare available quality assessment measures and illustrate which of them are most suitable for each satellite. Experimental analysis illustrates adequate assessment decisions produced by the selected measures for the results of representative pan-sharpening methods.

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

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.

Multi-sensor data fusion for urban area classification

Nowadays many sensors for information acquisition are widely employed in remote sensing and different properties of the objects can be revealed. Unfortunately each imaging sensor has its own limits on scene recognition in the sense of thematic, temporal, and other interpretation. Integration (fusion) of different data types is expected to increase the quality of scene interpretation and decision making. In recent time integration of synthetic aperture radar (SAR), optical, topography or geographic information system data is widely performed for many tasks such as automatic classification, mapping or interpretation. In this paper we present an approach for very high resolution multi-sensor data fusion to solve several tasks such as urban area automatic classification and change detection. Datasets with different nature are integrated using the INFOFUSE framework [1], consisting of feature extraction (information fission), dimensionality reduction, and supervised classification. Fusion of WorldView-2 optical data and laser Digital Surface Model (DSM) data allows for different types of urban objects to be classified into predefined classes of interest with increased accuracy. Numerical evaluation of the method comparing with other established methods illustrates advantage in the accuracy of structure classification into low-, medium-, and high-rise buildings together with other common urban classes.

Alphabet-Based Multisensory Data Fusion and Classification Using Factor Graphs

The way of multisensory data integration is a crucial step of any data fusion method. Different physical types of sensors (optic, thermal, acoustic, or radar) with different resolutions, and different types of GIS digital data (elevation, vector map) require a proper method for data integration. Incommensurability of the data may not allow to use conventional statistical methods for fusion and processing of the data. A correct and established way of multisensory data integration is required to deal with such incommensurable data as the employment of an inappropriate methodology may lead to errors in the fusion process. To perform a proper multisensory data fusion several strategies were developed (Bayesian, linear (log linear) opinion pool, neural networks, fuzzy logic approaches). Employment of these approaches is motivated by weighted consensus theory, which lead to fusion processes that are correctly performed for the variety of data properties. As an alternative to several methods, factor graphs are proposed as a new approach for multisensory data fusion. Feature extraction (data fission) is performed separately on different sources of data to make an exhausting description of the fused multisensory data. Extracted features are represented on a finite predefined domain (alphabet). Factor graph is employed for the represented multisensory data fusion. Factorization properties of factor graphs allow to obtain an improvement in accuracy of multisensory data fusion and classification (identification of specific classes) for multispectral high resolution WorldView-2, TerraSAR-X SpotLight, and elevation model data. Application and numerical assessment of the proposed method demonstrates an improved accuracy comparing it to well known data and image fusion methods.

Combined Haze and Cirrus Removal for Multispectral Imagery

Multispectral satellite images are often contaminated by haze and/or cirrus. A previous paper presented a haze removal method that calculates a haze thickness map (HTM) based on a local search of dark objects. The haze-free signal is restored by subtracting the HTM from the hazy image assuming an additive model of the haze influence. The HTM method is substantially improved by employing the 1.38-μm cirrus band. The top-of-atmosphere reflectance cirrus band is used as an additional source of information. The method restores the information in highly inhomogeneous surfaces attenuated by a low-altitude haze and high-altitude cirrus, improving the interpretation of the scene content while preserving the shape of the spectral signatures. The new enhanced HTM method is successfully applied to Landsat-8 OLI and Sentinel-2 real and simulated scenes.

Fusion of reconstructed multispectral images

A new technique for fast fusion of multiresolution satellite images with minimal colour distortion is presented in the paper. The technique allows to reconstruct multispectral images with resolution higher than resolution of the panchromatic image. Combination of image super-resolution restoration and image fusion based on global regression was applied. Super- resolution image restoration is based on simultaneous processing of several multispectral images to reconstruct a panchromatic image with higher resolution. This method is quasi-optimal on minimum squared errors of image restoration.

Selection of numerical measures for pan-sharpening assessment

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.

The hyperspectral sensor DESIS on MUSES: Processing and applications

The hyperspectral instrument DLR Earth Sensing Imaging Spectrometer (DESIS) will be developed and integrated in the Multi-User-System for Earth Sensing (MUSES) platform installed on the International Space Station (ISS). The DESIS instrument will be launched to the ISS mid of 2017 and installed in one of the four slots of the MUSES platform. The MUSES / DESIS system will be commanded and operated by the publically traded company Teledyne Brown Engineering (TBE), which initiated the program. TBE provides the MUSES platform and the German Aerospace Center (DLR) develops DESIS and establishes a Ground Segment for processing, archiving, delivering and calibrating the data used for scientific and humanitarian applications. Harmonized products will be generated by the Ground Segment established at Teledyne. This article describes the processing ground segment and the foreseen data validation activities. Finally comments regarding the data policy and foreseen scientific uses are given.