Abstract
The basic knowledge about the differences between multi- and hyperspectral data is provided and the potential of hyperspectral image analysis is highlighted. Relevant pre-processing steps and different ways to analyze hyperspectral data are presented. The chapter closes with a short outlook on expected developments with relevance for urban applications.
The basic knowledge about the differences between multi- and hyperspectral data is provided and the potential of hyperspectral image analysis is highlighted. Relevant pre-processing steps and different ways to analyze hyperspectral data are presented. The chapter closes with a short outlook on expected developments with relevance for urban applications.
FormalPara Learning ObjectivesUpon completion of this chapter, the student should gain an understanding of:
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1.
The principal differences between multi- and hyperspectral data
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2.
The need for a dedicated pre-processing of hyperspectral data
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3.
Analysis techniques with relevance for urban applications
1 Introduction: Hyperspectral Data and Urban Remote Sensing
Hyperspectral data, often also referred to as spectral high resolution data, imaging spectrometry data, or imaging spectroscopy data, have been successfully applied in different fields of terrestrial remote sensing since about 20 years (Goetz et al. 1985). However, only recently their value for urban applications has been put forward (e.g. Ben-Dor 2001; Herold et al. 2004). From a remote sensing point-of-view, the urban setting differs from natural or semi-natural environments due to a few distinct characteristics:
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Object heterogeneity or texture: Many urban features exhibit sharp borderlines, while their inner-object variance may vary substantially. A large parking lot with cars may appear extremely heterogeneous, while the neighboring industrial complex is represented by a few homogeneous roof constructions.
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Landscape heterogeneity and object size: Object size and heterogeneity are often interlinked. It is also sometimes difficult to specify average object sizes for a complex environment such as the city. However, the size of most objects (houses, cars, street width) may be regarded as relatively small (Small 2003), compared to other situations (agricultural fields, forest plots, open water surfaces). The amount of mixed pixels resulting from this circumstance varies, depending on the pixel size, but is usually much higher than in most other cases.
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Combination of natural and anthropogenic materials: Urban surfaces include a great variety of spectrally distinct surfaces. Urban areas may well include large areas consisting of natural materials (vegetation, soils, water), as “urban” is not necessarily defined through the built environment. Theoretically, mixtures of all natural and anthropogenic materials may occur.
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Geometric complexity: The application of airborne sensors and the associated wide field-of-view angles (compare 9.2) results in extreme differences in object illumination. A sensor records the shaded backside of built-up areas with scan angles opposite to the sun azimuth, scan angles parallel to the sun azimuth lead to a view on illuminated facades. The strength of this effect varies with sun elevation/azimuth, object geometry/spectral behavior, and flight direction, i.e. it is a spectrally varying function depending on sun-object-sensor geometry.
Details and explanations on the spectral and geometric behavior of urban surfaces are given elsewhere in this volume. However, even from this short introduction it becomes apparent that the analysis of such an environment provides an enormous challenge for remote sensing based data analysis, monitoring approaches, and thematic assessments. One of the options to tackle object and landscape heterogeneity is to employ high spectral resolution remote sensing data.
There is no precise definition of which number of bands separates multispectral from hyperspectral data. One may, for example, agree that sensors allowing for a detailed analysis of absorption features in their spectral range fall in the category of hyperspectral data. To date, there is no operational hyperspectral satellite sensor offering an adequate geometric resolution for urban applications. With the advent of the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) in 1987, the first airborne hyperspectral imager with 224 contiguous spectral bands between 400 and 2,500 nm was available for a wide range of applications. Sensors like the Digital Airborne Imaging Spectrometer (DAIS 7915) featuring hyperspectral thermal infrared capabilities offer additional prospects for urban analysis; however, thermal infrared devices for narrow band sensors offer a critical signal-to-noise ratio and a stable calibration appears difficult. For this chapter, examples from field or laboratory measurements, sampled with an ASD FieldSpec Pro II spectroradiometer (Hostert and Damm 2003), and a subset of HyMap data acquired in July 2003 over Berlin, Germany, (DLR 2003) are given.
2 Pre-processing
An adequate pre-processing of hyperspectral data is a mandatory prerequisite to extract useful information from hyperspectral data, regardless of working in urban or other environments. However, the analysis of urban properties must be regarded among the most demanding applications in terms of hyperspectral image pre-processing. This applies on one hand to the requirements for a precise co-registration with other raster or vector data sets (van der Linden and Hostert 2009). On the other hand, the spectral variability and complex illumination geometry ask for a precise definition of radiometric correction processes. It is therefore not surprising that pre-processing of hyperspectral data consists of a not to be underestimated series of processing steps, in terms of complexity as well as in terms of the amount of effort and time.
From the end-user point of view, pre-processing of remote sensing data can be divided into preliminary quality assessment, correction of bidirectional effects, geometric correction, and radiometric correction. A screening for spatial, spectral or radiometric errors should be performed to detect problematic regions of an image. Usually, bands with particularly low signal-to-noise-ratio (SNR) are discarded. Such a screening may also include further steps such as cloud and cloud shadow mapping or the definition of areas with uncharacteristic directional reflectance behavior (e.g. regions of specular reflectance in the case of water targets).
Most airborne scanners are characterized by a wide field-of-view (FOV) resulting in different directional reflectance behavior of similar targets depending on sun-surface-sensor geometry. As hyperspectral data are almost exclusively acquired with airborne sensors today, correcting for wavelength dependent bidirectional effects is obligatory for most analyses (Schiefer et al. 2006). This can be achieved by calculating and individually applying a view angle dependent and band-wise polynomial function in across-track direction, also referred to as “across-track illumination correction”. Mean column-wise reflectance values are calculated for each spectral band and differences interpreted as the scan-angle dependent variations in reflectance. It is obvious that such a simplistic approach does not account for land cover dependant differences in bidirectional behavior. As the urban environment is spatially extremely heterogeneous, a pre-classification in dominating land cover classes allows for a class-wise calculation and correction of directional properties. A view angle dependent correction results in comparable radiances of similar urban surfaces in across-track direction (Fig. 9.1).
2.1 Radiometric Correction
Usually, hyperspectral data will be distributed as scaled radiance values (e.g. in µW * cm−2 * nm−1 * sr−1) and calibration is carried out by the data provider. However, to compare hyperspectral imagery with field-based measurements and to open up the pathway towards quantitative analysis, radiance (variable with illumination) has to be converted to reflectance (invariable for comparable surfaces). This process is termed “radiometric correction”. Various methods of empirical and parametric radiometric pre-processing methods can be distinguished. A simple and useful approach is the empirical line correction method, relating spectral ground measurements with radiance values of the respective targets in the imagery. The urban environment offers abundant invariant and well identifiable targets, which may serve as input from the image. Applying the resulting band-wise transfer functions leads to values close to reflectance. However, due to the linear approach non-linear radiometric distortions will usually not be adequately corrected. Disturbance patterns that vary over the scene – especially the highly variable water vapor content – can also not be tackled. Nevertheless, for many cases empirical line corrected data may serve as a valid input for further processing steps.
If a more precise correction of radiometric properties is required, parametric approaches need to be implemented. Atmospheric properties are measured, modeled or estimated to pixel-wise invert the respective disturbance processes and result in reflectance values. Non-linear effects like the influence of second-order radiometric disturbances from the target environment can be incorporated via a window-based determination of scattering processes. Aerosol scattering in the shorter wavelength regions is corrected by applying pre-defined aerosol models and distributions along with appropriate aerosol scattering functions. The most problematic factor is the water vapor content that varies over short distances. As hyperspectral data sets are spectrally quasi-continual measurements including water vapor absorption bands, it is possible to determine the water vapor quantities by analyzing the absorption bands at wavelengths of 940 and 1,140 nm, which correlate well with water vapor quantities. A pixel-wise water vapor estimate from the image itself can hence be included in the correction process (Gao and Goetz 1990).
Finally a correction of topography effects is necessary to precisely account for illumination dependent differences. Direct and diffuse illumination along with shading effects largely varies the target reflectance properties. In an urban environment, the influence of topography and the influence of the built environment are to be distinguished. The first can be included via aspect, slope, shading, and visible sky view properties extracted from a digital elevation model (DEM). However, large scale geometric properties, such as building height or roof angles, are only provided by precise digital object models (DOM). Such models are available from high resolution stereo data, light detecting and ranging (LIDAR), or interferometric synthetic aperture radar (IFSAR). Though, state-of-the-art techniques do not yet allow for a geometric co-registration of these models and hyperspectral data in the cm-range, which would be necessary to apply the appropriate calculations. Nevertheless, a parametric radiometric pre-processing relying on an adequate parameterization of atmospheric parameters and including a DEM is the most accurate way of radiometrically correcting hyperspectral imagery (Fig. 9.2).
2.2 Geometric Correction
The geometric correction of airborne hyperspectral scanner data is similar to the geometric correction of multispectral scanner data apart from the amount of spectral bands to be rectified. Considering urban environments, the precise co-registration with cadastral data or similarly high resolution geometries is particularly demanding. The advantage may be that precise reference data often exist for urban environments, which is not necessarily the case for other settings.
In an ideal case, airborne data are provided as an image cube accompanied by an auxiliary data stream of differential global positioning system positions (DGPS) and inertial navigation system data (INS). The first provides sub-meter accurate position data of the sensor during image acquisition (x-, y-, and z-coordinates), the latter information on roll, pitch, and yaw movements of the platform (κ-, ϕ-, and ω-angles). Assuming a correct synchronization between scan lines and auxiliary data, it is possible to calculate the acquisition geometry for every pixel. A DEM has to be included to correct for terrain induced distortions (Schläpfer and Richter 2002).
It will usually be necessary to incorporate ground control points (GCPs) in this processing scenario to correct for inaccuracies in the measurements itself and for potential erroneous synchronization between data and auxiliary data. This is a rather straightforward task in the case of urban environments, as either ground-based DGPS measurements, orthophotos, or accurate vector data are available or may be retrieved (in the case of DGPS measurements) for many urban areas. The diversity and crispness of urban features supports the identification of accurate GCPs. Additionally, accurate ground truth allows for a high-quality assessment of geometrically corrected data sets.
3 Spectral Libraries
One of the most advantageous conceptual frameworks in hyperspectral remote sensing is based on the opportunity to relate field- or laboratory based spectrometric measurements with imaging spectrometry data from airborne or spaceborne sensors. The spectral behavior of distinct objects on the Earth’s surface is determined by their physical and chemical properties. While a few working groups have started to collect such spectra, the available databases are far from exhaustive (ASTER 1998; Ben-Dor 2001; Heiden et al. 2001; Hostert and Damm 2003). Recently, a structured approach to acquiring a more complete urban spectral library and to analyze material separability has been exemplified for the Santa Barbara region by Herold et al. (2004) and is illustrated in this textbook.
Measurements of the respective components under controlled conditions in the laboratory or under real-world conditions in the field can hence be related to the surface’s physical or chemical properties (quantitative approaches); alternatively, such measurements may serve as well-defined samples to identify similar components (qualitative approaches) in imaging spectrometry data. The ability to relate radiometrically corrected hyperspectral data from diverse sensors with ground-based spectroradiometric data can be regarded as a spectral upscaling.
Field or laboratory measurements are performed with spectrally very high resolution instruments. Spectra, or so called spectral endmembers, are usually normalized to reflectance values and stored in a spectral database, along with an appropriate set of meta-data. Spectral data may be combined with coordinate information in a geo-database to provide an urban spectral cadastre. While such data sets are abundant for many natural environments, there is still the need for more extensive urban spectral libraries that allow selecting a great range of very high resolution urban spectra from pre-defined sources. Once collected, very high resolution spectral references may be resampled to the spectral resolution of imaging spectrometers based on their band dependent sensitivity functions (Fig. 9.3).
4 Analysis Techniques
High resolution spectral data differ from multispectral data in their ability to detect subtle differences in surface components. While other sensor concepts focus on the utilization of different wavelength regions or fundamentally different acquisition techniques (e.g. radar sensors or sounding sensors), high resolution spectral data work in the same wavelength domains as most multispectral devices, but in very narrow spectral windows per band. As a consequence, the high number of bands not only offers different analysis options, but actually requires different analysis techniques. While conventional classification approaches may be utilized, comparable to those employed for multispectral data analysis, the full potential of such data is made accessible when more sophisticated or adapted methods are utilized. In the following a focus is put on data optimization, classification/material detection, and spectral mixture analysis.
4.1 Data Optimization
The high number of spectral bands can be regarded as an advantage and a problem at the same time. A high spectral autocorrelation between neighboring wavelengths leads to redundant information. Considering that hyperspectral data sets may easily grow to GByte sizes, processing performance will unnecessarily suffer, depending on hard- and software capabilities. While such problems will be overcome with more powerful tools, the ability to derive useful information from such data sets may also be impeded by redundant information. Data transformations are therefore a standard pre-processing option in cases when the original spectral information is not inevitably needed (e.g. for optimized classification).
The Minimum (or Maximum) Noise Fraction (MNF) is widely used to optimize hyperspectral data analysis. Comparable to a principal component analysis, an MNF transformation sorts the bands of a data set regarding variance explanation. It then decorrelates the noise content in the data and orthogonalizes feature space (Green et al. 1988). The resulting MNF bands with low noise components may then be analyzed during further processing steps (Fig. 9.4).
Alternatively, the first bands that are considered to be noise-free may be extracted and inverted again to yield noise-free reflectance data. It has to be remarked that such a procedure has always to be considered in the light of the analysis goal. Depending on the original feature space and the thematic question at hand, important information may be found in less important MNF bands and a careful screening of individual bands is necessary before either spectrally subsetting or inverting subsetted data. In any case, a transformation of spectral library information is also mandatory when using transformed data along with ground-based spectrometry.
4.2 Classification and Material Detection
In principal, the same fundamentals apply to the classification of multispectral and hyperspectral data sets. Well known supervised and unsupervised classification techniques will hence not be considered here. More recent developments, such as the use of image segmentation and object oriented analysis techniques, are also applicable to spectral high resolution data and described elsewhere in this volume. In this chapter, a focus is put on those methods that are more often used with hyperspectral data or that appear particularly advantageous when applied with hyperspectral data.
There are numerous techniques focusing on either the ability to detect absorption features in surface materials from imaging spectrometer data or on the extended feature space of hyperspectral imagery as a whole (or MNF-transformed input). Absorption based detection of single materials originates from geological applications, but is also useful in urban environments, where diverse and spectrally distinct materials occur. This capability of spectrometric data is generally enhanced by normalizing spectra via a so-called convex-hull transformation. A mathematically derived curve is fitted to envelop the original spectrum (hull), utilizing local spectral maxima to connect the hull segments, while leaving absorption features as spectral gaps below the hull. Dividing the original spectrum by the hull values results in a baseline along 1 (or 100% of the hull) and relative absorption features with depths between 0 and 1 (Fig. 9.5). These features are quantifiable in a sense that for example the absorption depth or the full width at half maximum (FWHM) of the absorption feature can be measured regardless of potential albedo differences in the individual materials.
It is then possible to compare transformed spectra from imaging spectrometry data with equally processed spectra from a spectral library. This may be done by calculating the band-wise residuals between image and reference spectrum and cumulating these in a root mean squared error (RMSE). A perfect match (which is a rather theoretical assumption) should yield in zero residuals and would indicate image areas that are 100% pure concerning the respective material. Usually, even pure materials will not perfectly fit library spectra due to diverse error components (measurement setup, SNR, directional reflectance differences, calibration, atmospheric correction, etc.). It is very likely that the majority of image pixels will rather be mixed than pure in urban environments. Absorption features will therefore be masked or enhanced by other material characteristics on one hand and new absorption features may appear on the other hand. There is in any case the need to account for such effects beyond the RMSE as a global measure of spectral fit. Individual absorption feature depth or FWHM comparisons between image and reference spectra may hence serve as a measure of material abundance in mixed pixels.
As view angle dependent effects are critical in urban environments and illumination geometry is complex, it might be advantageous to employ methods that are fairly insensitive to illumination effects. Spectral angle mapping (SAM) is such a technique. Differently from other classification techniques, SAM compares reference signatures with individual pixels not by their statistical representation in feature space per se, but by their angular differences in feature space position. Considering multidimensional feature space as axes starting from a zero-reflectance point, reference signatures and pixels are aligned along these axes and the multidimensional angle between all references and the respective target pixel are calculated. This angle is independent from changes in pixel albedo, as all pixels of the same spectral character exhibiting for example illumination differences will align along the same vector starting from the zero reflectance point. As the vector direction does not change the angle between a reference target and a pixel vector is fixed either.
4.3 Analysis Focusing on Mixed Pixels
Ridd (1995) has proposed a conceptual framework to analyze urban remote sensing data based on the major urban surface components vegetation, impervious surfaces and soil. This model has became a kind of standard concept for many remote sensing based analysis approaches focusing on the urban environment. Authors like Phinn et al. (2002) have shown that such an approach can be successfully transferred to an analysis at subpixel level. Hyperspectral data are well suited for applying such methods, as their spectral information content allows the discrimination of diverse materials in a pixel. This paragraph provides an overview on the analysis of urban areas with methods capable to quantify material components at a sub-pixel level, commonly referred to as spectral mixture analysis (SMA) or spectral unmixing.
A straightforward unmixing procedure is a linear spectral unmixing approach. In a simplifying approach, pixel reflectance is supposed to depend on a linear combination of a limited set of pure urban surface components (or endmembers) and can hence be decomposed by calculating the respective fractional component abundances.
For statistical reasons, the maximum number of possible endmembers depends on the dimensionality of the data set and the spectral contrast of the individual endmembers. It will usually be close to the independent feature space bands (represented, for example, by an MNF-transformation). Potentially, this leads to uncertainties in the unmixing process and to unexplained surface components not represented by a limited number of endmembers. The unexplained components are accounted for by band-wise residuals; residuals may be summarized as root mean squared error (RMSE). The RMSE is particularly relevant in highly diverse urban areas to keep track of uncertainties in the data analysis. A second indicator is that the resulting fraction image for every endmembers contains positive values only. Every unmixing model should sum to unity, which is mathematically also possible with endmember abundances below zero or above 100%. Largely positive fractions indicate the validity of the unmixing, as the mathematical solution represents physically meaningful results (Fig. 9.6).
Assuming that suitable spectra are available in a spectral library, one way to overcome this limitation is to employ multiple endmember models, i.e. to use individual endmember combinations depending on the respective components present in every individual pixel. It may, for example, be adequate to model a pixel in a homogeneous industrial area with two endmembers only, e.g. concrete and asphalt. Heterogeneous urban areas such as many residential quarters may result in pixels containing much more surface features such as grass, asphalt, concrete, roof shingles, and colored metal surfaces from cars. It is then possible to either leave it to the software to define appropriate endmember models for each pixel or to define possible combinations in advance and only chose among those.
5 Future Developments
Imaging spectroscopy is at present a tool largely driven by technological improvements. In the near future, advanced spectrometers will emerge that will open up the road to new analysis tools and new ways to employ them. Such sensors will enhance our ability to differentiate materials or to model quantitative indicators from primary parameters like surface reflectance. One of these near future developments is the Airborne Reflective and Emissive imaging Spectrometer (ARES) with 155 spectral bands including the thermal infrared and an excellent SNR (Wilson and Cocks 2003). Also, spaceborne high resolution spectrometers with satisfactory SNR will become available in a few years, such as the Environmental Mapping and Analysis Program (EnMAP, Buckingham and Staenz 2008).
Moreover, the combination of hyperspectral with other remote sensing data and enhanced analysis techniques offers a high potential of further improvements in data analysis. Sensor integration may include data fusion concepts between very high geometric resolution and hyperspectral data (Lehmann et al. 1998). Such sensor combinations are particularly valuable for urban applications as an improved geometric resolution will result in less mixed pixel surfaces. From a processing point-of-view, combined analysis schemes such as the integration of supervised classification and spectral unmixing (Segl et al. 2000) or the use of machine learning classifiers (van der Linden et al. 2007) offer new opportunities, especially in the heterogeneous urban environment. Finally, it has to be remarked that quantitative analyses and modeling approaches will become more relevant in the future. While there are examples of quantitative models of soil or vegetation properties (e.g. Schlerf et al. 2005) such approaches have not yet been implemented for urban applications.
Learning Activities
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Download the trial version of The Environment for Visualizing Images (ENVI): http://www.ittvis.com/Academic/Students/ENVIStudentEdition.aspx. Use the online help system to learn more about the different options to analyze hyperspectral data.
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Read Goetz et al. from the list of references to get an insight on imaging spectroscopy.
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Read Section 4 (Urban and Land Use Applications) and Section 13 (Collecting Data at the Surface – Ground Truth; The “Multi” Concept; Hyperspectral Imaging Spectroscopy) in the NASA Remote Sensing Tutorial: http://rst.gsfc.nasa.gov/Front/tofc.html.
Exercises
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Discuss in how far multispectral and hyperspectral data differ. Why are these differences particularly useful in analyzing urban remote sensing data?
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Why is it mandatory to perform a radiometric pre-processing of hyperspectral data to compare spectra with those from a spectral library?
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Access the ASTER spectral library (see list of references). Compare the available list of urban materials with the prevailing materials of an urban area you are familiar with. Evaluate the completeness of the database.
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Use the trial version of ENVI to load the John Hopkins University spectral library (choose: Spectral – Spectral library viewer – Open Spec Lib; change to the folder jhu_lib and chose manmade2.sli; confirm your choice with ok). Click the “red smooth-faced brick” (a plot window will open to visualize the spectrum) and then “Reddish asphalt roofing shingle”. Why would Spectral Angle Mapping not necessarily be the best way to separate these materials?
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A modeling approach for simulating the urban heat island requires an estimate of the amount of different roofing materials. Which analysis methods could be chosen to extract such information from hyperspectral data?
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Fig. 9.3 shows a cobblestone pavement in different spectral resolutions. Use this figure to explain why hyperspectral resolution is needed to employ feature based analysis techniques.
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Comparing Figs. 9.4 and 9.1, it is obvious that the 3-band representation in Fig. 9.4 contains more differentiated information. What does this mean for an analysis process based on original and MNF-transformed data? Why is it not appropriate to employ an MNF-transformation when considering feature based analysis concepts?
5.1 Chapter Summary
Hyperspectral remote sensing data differ from multispectral data in the number of spectral bands and hence in the analysis options associated with such data. These extended analysis opportunities are on one hand particularly useful in a heterogeneous urban environment. On the other hand, this heterogeneity results in demanding pre-processing schemes and accuracy level that need to be achieved. Radiometric pre-processing focuses on illumination and atmospheric corrections. As all hyperspectral data used for urban applications are acquired by airborne sensors nowadays, the geometric pre-processing including DGPS and INS information is demanding.
The huge number of bands and information redundancy might also require data optimization, such as MNF transformations. Relevant analysis approaches include material detection techniques, spectral angle mapping, or spectral mixture analysis. Hyperspectral data will further gain in importance in the future with the advent of new sensors and dedicated analysis options.
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Hostert, P. (2010). Processing Techniques for Hyperspectral Data. In: Rashed, T., Jürgens, C. (eds) Remote Sensing of Urban and Suburban Areas. Remote Sensing and Digital Image Processing, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-4385-7_9
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