DZD DPZ 10 Image spectroscopy. Doc. Dr. Ing. Jiří Horák - Ing. Tomáš Peňáz, Ph.D. Institut geoinformatiky VŠB-TU Ostrava

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1 DZD DPZ 10 Image spectroscopy Doc. Dr. Ing. Jiří Horák - Ing. Tomáš Peňáz, Ph.D. Institut geoinformatiky VŠB-TU Ostrava

2 Basic info Hyperspectral data contains both spatial and spectral information from materials within a given scene. Each pixel across a sequence of continuous, narrow spectral bands, contains both spatial and spectral properties. Pixels are sampled across many narrowband images at a particular spatial location within the "spectral cube", resulting in a one-dimensional spectrum.

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5 Data from image spectroscopy hyperspectral data Continuous interval of EMG spectra Hundred narrow, neighbouring image brands (i.e. AVIRIS 224) Band width 10 nm or even less It can be used to identify, and in some cases characterize scene features based on their unique spectral signatures (absorption bands or emissive features). Ultraspectral sensors s of spectral channels, each with a bandwidth narrower than those of hyperspectral sensors (less than 5 nanometers). Ultraspectral sensors - allow a quantitative assessment of scene materials (solids, liquids and gases). For example, the abundance of different gases or effluents could be determined based on the width and strength of absorption features in a given spectrum. Ability of subpixel detection (in case of high contrast) Measurements by image spectroscopy (spectro-radiometers) analysis and evaluation - special processing procedures

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7 Comparison of spectral curves Modis, TM, HSD Alunite feature

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9 Identification of gases temperature contrast is required

10 Process of analysis of hyperspectral data 1. Preprocessing (calibration) atmospheric correction. Critical step, be aware of 1st type errors (false positive) due to bad corrections 2. Vizualization and data adjustment selection of suitable bands (elimination of noisy bands, elimination of bands with redundant information) i.e. MNF transformation (2 PCA) 3. Derivation of spectral endmembers. The endmember stands for clear spectral signal of one material. It is required to detect endmembers in data, using spectral libraries or using field measurements (creating own spectral libraries). 4. Spectral mapping. Creating the final product: Scene classification into thematic maps, Map of material identification, Map of target detection

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12 Method of processing hyperspectral data according to NIKM Výběr území Výběr dat DPZ Hyperspektrální družicová data Hyperspektrální letecká data Předzpracování - Radiometrické a geometrické korekce Pozemní měření v době přeletu Atmosférická korekce - empirická Atmosférická korekce - modelová Předzpracování určení transformačního klíče pro georeferencování Analýza hyperspektrálních dat po atmosférické korekci - negeoreferencovaná scéna Výběr příznakových oblastí známá KM Referenční spektrální knihovna laboratorně měřených kontaminantů Kontrola odlučitelnosti vybraných oblastí (analýza příznakového prostoru) Volba metody obrazové spektroskopie Metoda nejmenších čtverců (SFF) Metoda spektrálního úhlu (SAM) Maskování Spektrální analýza snímků Georeferencování výsledků analýz Identifikace potencionálně kontaminovaného místa Aplikace GIS

13 Collection of hyperspectral data Satellite (EO-1 Hyperion) Airborne (AVIRIS)

14 EO-1 Hyperion Spatial resolution 30 m; 220 neighbouring narrow spectral bands with spectral resolution of 10 nm. Spektral range of sensor is from 357 to 2576 nm. Ground scene is 7,5 km x 100 km. Due to sensor degradation only 158 channels are calibrated for VNIR (visible and NIR) and for SWIR (small-wave IR band). Other channels are not calibrated due to low sensitivity and they are set to 0. USGS archive contains data from the 1st year of operation (demonstration/validation mission ), as well as from the EO-1 Extended Mission (since 2001). Scenes are available in L1Gst level of processing, GeoTIFF and free of charge. Following levels of Hyperion data preprocessing are available: 1. Level 0R (L0R) no corrections. 2. Level 1R (L1R) radiometric correction. 3. Level 1Gs (L1Gs) radiometric corrections, resampled after geometric corrections and transformed to suitable projection 4. Level 1Gst (L1Gst) radiometric corrections, resampled after geometric corrections and transformed to suitable projection. Orthorectified image using DEM for elimination errors. DN in L1G is 16 bit signed integer.

15 Available scenes of Hyperion and testing areas for NIKM in NIKM

16 Examples of image spectrometers for scientific and commercial usage Senzor Organization Country Bands wavelength [µm] AVIRIS NASA USA 224 0,40 2,50 AISA Spectral Imaging, Ltd. Finsko 286 0,45 0,90 CASI Itres Research Kanada 288 0,43 0,87 DAIS 2115 GER Corp. USA 211 0,40 12,50 HYMAP PROBE-1 Integrated Spectronics Pty Ltd. Earth Research Sciences Inc. Austrálie 128 0,40 2,45 USA 128 0,40 2,45

17 Collection of hyperspectral data AVIRIS airborne hyperspectral imaging sensor obtains spectral data over 224 continuous channels, each with a bandwidth of 10 nm over a spectral range from 400 to 2500 nanometers. An example of an operational space-based hyperspectral imaging platform, is the Air Force Research Lab's TacSat- 3/ARTEMIS sensor, which has 400 continuous spectral channels, each with a bandwidth of 5 nm.

18 Mechanooptický spektrometr

19 Vizualization of hyperspectral data Curvatures of spectral behaviour Graphical representation in spectral space Image cube

20 Curvatures of spectral behaviour Absorption bands Typical shape of curvatures for some materials identification discrimination Libraries of curvatures of spectral behaviour: SW - ERDAS Imagine, ENVI, TNTmips, ER-Mapper www:

21 Curvatures of spectral behaviour

22 Curvatures of spectral behaviour for vegetation

23 Curvatures of spectral behaviour

24 Libraries of curvatures of spectral behaviour

25 Image cube

26 Obrazová kostka

27 Image cube fuzzy zones areas of atmospheric water absorption (1,4 and 1,9 µm)

28 Reflectivity in band 2 Mixed spectra endmembers A, B, C clear spectral behaviour Mixed covers mixed (image) space Different way of understanding mixed covers macroscopic view detail (microscopic) view endmember A mixed space endmember B endmember C Reflectivity in band 1

29 Mixed spectra microscopic view Background photon interacts with several types of material (in the mixture) Mixed spectrum nonlinear combination (difficult description of individual shares)

30 Mixed spectra macroscopic view Background photon interacts with one type of material, cover Mixed spectral reflectance linear combination

31 Mixed covers (surfaces)

32 Mixed pixels and material maps Input image CLEAR CLEAR Mapping of red share CLEAR MIXED Mapping of green share

33 Preprocessing of hyperspectral data

34 Preprocessing of hyperspectral data Requirements of analysis of hyperspectral data atmospheric corrections corrections of local conditions (influences of terrain complexity) Noise data elimination

35 Preprocessing of hyperspectral data methods of reflectivity conversion for hyperspectral data flat field conversion conversion of average relative reflectance method of empirical line modelled methods Noise elimination in data Principal component analysis (PCA) Transformation of minimal noise fraction (MNF)

36 Influence of atmosphere to sun radiation

37 Flat Field Conversion Flat Field Conversion Average spectral response of the field is influenced by Solar irradiance atmospheric variance and absorption Image record from homogeneous area so called Flat Field flat spectral curvature resp. its part Normalization of the image of spectral characteristics of the scene Conversion of the scene into relative reflectance divided by average reflectance of flat field

38 Flat Field Conversion Selection of light areas Method suitable for homogeneous fields deserts dry basin of salt lake Light anthropogenous materials (concrete in towns) method unsuitable for land with significant changes of the altitude (majority of the scene) Topographic Shading Atmospheric Path Differences

39 Average Relative Reflectance Conversion Normalization of spectral characteristics using dividing by average value for the whole hyperspectral record Such preprocessing will remove: topographical shadows changes of brightness intensity

40 Average Relative Reflectance Conversion Assumption enough heterogeneity of records: eliminate spatial variability represented by spectral characteristics calculate average value of spectral characteristics (similar to flat field conversion) Suitable only for some regions May create spurious spectral characteristics

41 Other methods of spectral reflectance conversion Focused on mineral mapping Influence of atmosphere to the change of spectral reflectance: small - infrared <2; 2,5>μm neglectable visible and near infrared <0,4;2,0>μm In case of dark materials and deep topographical shadows use approximate following correction of bands: Adjust minimal brightness value or average value in the shadow Subtract from each DN value

42 Empirical Line Method Used for conversion of image data (DN) into ground reflectance Measurements of ground spectral reflectance in two or more areas: Satisfactory enough, means distinguishable in the image Show substantially different DN values Relationship between spectral reflectance on the ground and above the atmosphere

43 Empirical Line Method offset additive component: Do not include topographical effects: Differences in distances of passing through atmosphere Topographical shadows

44 Modelling methods Radiance conversion simulation of sun radiance spectrum according to the sun elevation Input atmospheric conditions: known from field measurements Unknown from estimation (using distribution of CO 2, O 2 ) Mixture of CO 2, O 2 Scattering effect of water vapour estimation and correction and water absorption Topographical shadows may be included Example - model ATREM 3.0

45 Modelling methods ATREM 3.0 (Atmosphere Removal Program)

46 Data modification (enhancement) data preparation before classification

47 PCA Elimination of data redundancy - numerical and visual similarity of neighbour bands Information concentration in low level synthetic bands

48 PCA Example of PCA usage for AVIRIS PCA 1, 2, 3 PCA 6, 9, 12

49 Cross-correlation method Comparison of investigated band values with values of other bands Calculate correlation coefficients for same pixels The output is a correlation raster of DN <0; 1> Processing by thresholding: 0 no matching 1 perfect match, pixel represents an elementary cover Robust method to brightness heterogeneity in records

50 Minimal Noise Fraction Transformation (MNF) Preliminary noise calculation in each band using spatial deviation in brightness values 2 PCA follows Output synthetic bands with equally distributed noise Bands with lower indexes contains: dominance of image information Small (neglectable) amount of noise

51 Establishing of elementary surfaces in the image - Pixel Purity Index Pixel Purity Index Value of elementary surfaces are located along the boundaries of clouds of values in scatter plot

52 Establishing of elementary surfaces in the image Input for PPI calculation if the result of MNF - synthetic bands of low level perform the linear unmixing PPI is available in: TNTMips ENVI PPI raster as a mask for n-dimensional visual tools: TNTMips (n-dimensional Visualizer ) ENVI (n-d Visualizer )

53 Algorithm steps: Pixel Purity Index Generate testing vectors of random directions starting at the origin of scatter plot Setting threshold values for outlier determination Testing of extreme values in the cycle for different vectors. Output - PPI raster DN number of pixels between tested extremes PPI raster: the lowest DN dark pixels the lowest pure (mixed) spectra the highest DN light pixels the most pure (typical) spectra

54 Pixel Purity Index

55 Selection of elementary surfaces n-d Visualizer

56 odrazivost v pásmu 2 Visualizing of spectral characteristics vector variable 2-dimensional example 0,7 odrazivost v pásmu 1 0,8

57 Classification Methods of hyperspectral data Classification by spectral angle (SAM) matched filters MF) Spectral Template Matching Spectral Feature Fitting (SSF) Adaptive Coherence Estimator (ACE) Mixture Tuned Matched Filter (MTMF) linear unmixing (LU) CEM MTMF/SAM

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59 Band B Spectral Angle Mapper scheme for 2 bands S T β T vector of tested profile S vector of compared profile β spectral angle Band A

60 Classification by spectral angle Output is the raster where DN represents β Values of reflectance should be used but using radiation values will not cause serious errors The method is relatively insensitive for illumination and albedo effects The method do not require illumination corrections: Require the vector orientation (direction) Do not require a length of the vector

61 Matching of spectral profiles Matched Filtering, Matched Filters MF Method of analysing of heterogeneous pixels maximize the response of target spectra while supressing background clutter criterion - goodness of variance fit Measurement of similarity between: spectral profile of the explored pixel Compared end members

62 Matched Filtering

63 Matched Filtering The partial result is raster (for each compared spectral profile - end member) Date type in this raster floating point DN = 1 => perfect match DN < 1 => partial match The final result is the map of material (raster)

64 Matched Filtering partial rasters kaolinite chalcedony alunite

65 Material map (output of Matched Filtering MF)

66 Using spectral information for data analysis Limited usage of supervised classification Set of other methods DN of final raster represents the measurement of similarity between analysed and compared spectra The risk of material exchange by those with similar spectral behaviour

67 Spectral Feature Fitting (SFF) These methods require to transform data into reflectance and then to normalise spectral behaviour (elimination of continuum). The continuum represents the spectrum of surroundings which should not be taken into account for specific absorption properties of interested material. The spectral depth is proportional to the quantity of material in the sample and the grain size of the sample. The depth is increased with increasing the grain size but decreasing when absorption prevails scattering. The apparent depth of absorption D is relative to surrounding continuum: D 1 R R b c R b is reflectance in the centre of absorption band (minimal value after elimination of continuum), R c is reflectance of continuum for the wavelength of the centre of absorption band FWHM;

68 Spectral Feature Fitting (SFF) Spectral behaviour (manifestation) of explored material measured by remote sensing are not such good as manifestation of pure reference material. Illumination, terrain slope and atmospheric influences Unable direct comparison of referenced material spectra and explored material. Real material are almost always mixed comparison after separation of explored spectrum. Intensity is always lower for measurement in remote sensing necessity to correct values using changes of spectral contrast of referenced spectrum with eliminated continuum Lc k Lc' 1 k Where L c is measured spectrum, L c ' is normalized spectral behaviour with eliminated continuum (reflectance) best fitted to explored spectrum (it means its contrast k is modified). When k is less than 0 the spectral contrast is increased.

69 Spectral Feature Fitting (SFF) Another possibility how to write this equation: Linear solution of spectral depth thus it is possible to find direct solution without iteration. Necessary to find values of variable coefficients a and b providing the vest results for given spectral behaviour O c. Using standard Minimal Least Squares. Finally determine correlation coefficient F c bl c a L ' bb' F n L L n L O O L b c o c c c c 2 2 n O O n L O O L b c c c c c c 2 2 '

70 Adaptive Coherence Estimator (ACE) ACE models the background clutter using the data's statistics (covariance matrix). ACE is commonly used as a target finding technique since one does not have to have knowledge of all the endmembers within a given scene, and because the method does not depend on the relative scaling of input spectra

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72 Mixture Tuned Matched Filter (MTMF) Combine MF technique and linear mixture theory. It contains infeasibility parameter which describes how feasible is false positive error Useful mainly for sub-pixel analysis of material in the scene

73 Linear Unmixing (LU) Method of analysis of heterogeneous pixels Suppose knowledge of: spectral profiles of each material Content of each material in the pixel in percentage Measure of similarity between the spectral profile of investigated pixel and compared spectral profiles (end members)

74 Linear Unmixing Determine the share of elementary surfaces (end members) in the pixel Assumption The mixed spectrum was created by linear combination of end members macroscopic view to mixing Relatively small number of materials with constant properties requirement of selection of suitable end members: Using pure elements in the full range (each material)

75 Linear Unmixing (LU)

76 Linear Unmixing (LU) Solve a system of N linear equation for each pixel (N is the number of spectral bands) Result is the abundance of each searched material in one pixel expressed as a share Ability to identify materials which are not able to distinguish in the image This is an example of what is known as "Non-literal" analysis, in contrast to literal analysis where objects are identified by eye.

77 Linear Unmixing Result is the raster for each compared end member Each resulting raster shows the share of each spectral profile in explored raster DN = <0;1> resp. DN = <0;100>% ideal case: Value of share of end members in the interval <0; 1> Sum of share of end members = 1 resp. 100%

78 Evaluation of Linear Unmixing results Unsuitable parameters: Too much end members Unsuitable end members Sum of share of end members <> 1 error raster DN express: Residual error (RMS Error) high error values low coincidence necessity to include more end members

79 Applications

80 Better identification of red edge The high spectral resolution of hyperspectral sensors allows the clear identification of the "red edge" feature of healthy vegetation.

81 Burn index BI Mapping of burn scars and hot spots (seen as orange and bright orange spots on the right image) through smoke resulting from wildfires. The smoke is more transparent in the SWIR bands than in the VNIR bands. Using a contrast ratio of two different SWIR bands, a Burn Index (BI) can be created to measure the severity of burn scars. Data Hyperion

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83 Mapping of minerals one of the major applications of hyperspectral imaging where high spectral resolution is necessary to identify specific minerals from their unique absorption features produced by the interaction of radiation with the mineral's unique crystalline structure. a Matched filter was used along with a USGS reference spectrum of the water-alteration mineral Kaolinite, to detect is location at Cuprite, Nevada. In the MF detection map, the white areas indicate the presence of Kaolinite. The Minimum Noise Transform (shown in lower left image) reveals the diversity of minerals at the Cuprite, Nevada calibration test site. The top left pane shows the difference between the USGS reference spectrum (blue line) and the actual AVIRIS spectrum (red line). The fit to the specific absorption doublet feature at slightly less than 2.2 microns indicates the identification of the mineral Kaolinite. The SWIR portion of the spectrum between 2.0 and 2.5 microns is most commonly used to map minerals.

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85 Mapping of minerals 2 The following slide shows Matched Filter detections of three different alteration minerals at the Cuprite, Nevada site. Kaolinite, Alunite, and Buddingtonite are shown as different color overlays on top of a single baseline SWIR band.

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87 Detection of oil materials Hyperspectral imaging is especially useful for assessing environmental disasters, such as the 2010 Gulf Oil Spill. The location of oil slicks floating on the surface of ocean water can be identified using several unique absorption bands due to the C-H bond of the hydrocarbon. Small amounts of oil are sensitive to the 2.3-micron absorption feature, which is caused by different rotational modes of the hydrocarbon molecule. Thicker amounts of oil are sensitive to the 1.73-micron absorption feature, which is the result of the hydrocarbon molecule's strech mode. In contrast to multispectral imaging, which can locate oil slicks by their distinctive color on ocean water, hyperspectral imaging allows a quantitative assessment of the amount of oil present

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89 Military applications The high spectral resolution of hyperspectral sensors allows one to discriminate not only camouflage from background clutter, but different types of camouflage. Note the common spectral feature of two types of camouflage. They all "mimic" the red edge of vegetation, so they would all appear to blend in with background vegetation if they were imaged using conventional NIR/Red/Green multispectral imaging systems. However, hyperspectral imaging systems with expanded spectral coverage in addition to higher spectral resolution can differentiate the different types of camouflage, especially when examined in the SWIR portion of the spectrum. The SWIR bands also allow the discrimination between the two types of camouflage and the background vegetation.

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91 Future of image spectroscopy Using active hyperspectral systems with own source of controlled illumination Reduce or eliminate problems connected with artefacts of sun illumination and shadows