DZDDPZ8 Fourierova t., spectral enhancement. Doc. Dr. Ing. Jiří Horák - Ing. Tomáš Peňáz, Ph.D. Institut geoinformatiky VŠB-TU Ostrava

DZDDPZ8 Fourierova t., spectral enhancement Doc. Dr. Ing. Jiří Horák - Ing. Tomáš Peňáz, Ph.D. Institut geoinformatiky VŠB-TU Ostrava

Fourier transforms based on data transformation using harmonic signals = compositions of sin and cos trigonometric functions with different amplitudes and frequencies. Each continuous function can be Fourier transform Dobrovolný

Fourier transforms The result can be displayed as Fourier spectrum identified low frequencies are recorded near the spectre centre high frequencies are recorded near the edges orientation of edges or lines in the image is also important they are displayed perpendicularly to the original direction in the Fourier spectrum, the horizontal lines appear as vertical lines Shades of grey in the Fourier spectrum indicate the count of the respective frequency. Dobrovolný

Procedure of Fourier transforms the image is transformed into the Fourier spectrum using fast Fourier transforms (FFT) suitable filters are applied to this spectrum; the result is then transformed back into an image using inverse Fourier transforms (IFT) remove noise - apply a circular filter to the spectrum, keeping only the inner part of the circle (low frequencies). to highlight high frequencies - use a circular filter retaining the outer part of the spectrum. to cancel lines of a certain direction - apply a wedge filter of perpendicular direction. Dobrovolný

Dobrovolný

Spectral enhancement manipulation with multi-images division of the image and the use of ratios of image bands we can distinguish subtle spectral changes highlight changes in the inclination of spectral reflectance curves regardless of the absolute values From n bands IT - create n*(n-1) ratios or 1/2 (regardless of order).

Index characteristics Decrease the influence of topography different DN values due to variously illuminated slopes Calculation of spectral indices to enhance vegetation or some minerals Generally, the numerator contains the band which is intensely reflected by the highlighted surface while the denominator contains the band that is significantly absorbed by the given surface (Dobrovolný, 1998). Examples: TM4/TM3 as a vegetation index, TM3/TM1 as an index highlighting soils containing iron oxides ( red soils ), TM5/TM7 as an index highlighting soils with higher content of clay minerals

optimum index factor Synthesis from bands with the most different information OIF s k standard deviation of the k-band r j correlation coefficient between two included bands Composition (synthesis) with the highest OIF provide the greatest benefit (theoretically) Recommendation what bands should be included

If the final image contains only ratios for synthesis, the information on absolute values may be missing. If one original band is also used, we get a hybrid synthesis. As with other forms of image enhancement, preprocessing is recommended. Especially when using ratios, it is necessary to remove noise such as atmospheric haze

Vegetation indexes Usually ratios Most frequent usage: Assessment of the green biomass occurrence Indicator of the overall change in the environment (drought) Various vegetation indices normalized VI, transformed VI NDVI TM4 TM4 TM3 TM3

Principle of the calculation of vegetation indices Based on High interaction of health vegetation with VIS and NIR radiation specific spectral behaviour of vegetation (see basic course of RS!) Different behaviour of other materials (land cover)

Classification of vegetation indexes Vegetation indexes empirical optimalized Vegetation indexes (Jackson, Huete, 1991) Slope-based ratio ortogonal transformed

Empirical vegetation indexes Detection of vegetation occurrence The result has no exact physical meaning Not quantitative assessment but only indicators empirical based - obtained from various experiments Simple and fast calculation Not dependent on sensor Sensitive to some parameters (humidity, illumination) but they are not crucial

Optimized vegetation indexes Optimized to evaluation of the specific characteristics of vegetation The term index has historical roots and frequently they are not indexes according to the way of calculation (no ratio) More difficult calculation Sensor dependent Result is the quantitative variable Enable to compare imaged obtained from different sensors and for different places

Slope-based vegetation indexes VI is based on the ratio R/NIR reflectancies which create a semi-line from the axe origin (axes R and NIR reflectance)

Slope-based VI ideally the slope of the line R-NIR should relate to the quantity of vegetation β0 free soil β1 free soil, traces of vegetation β2 soil dominates + vegetation occurence β6 full coverage of vegetation

Ratio-based VI Comparison of the reflectance in R and NIR Various modifications RATIO, NDVI, RVI, NRVI, TVI, CTVI, TTVI

Simple Ratio (SR) Ratio Vegetation Index (RVI) SR NIR R SR NIR VIS Sensitive to divide by zero Proposed for Landsat MSS Separates vegetation from the soils in the background High values are caused by: Intensive absorption of R radiation by chlorophyll Intensive reflectance of NIR radiation by internal structure of the leaf

Normalized Differential Vegetation Index NDVI TM4 TM4 TM3 TM3 NDVI NDVI Designed for Landsat MSS (1974), and other sensors Separates green vegetation from soils AVHRR 2 AVHRR 2 Higher values indicate massive occurence of vegetation Rich vegetation positive values (0.3-0.8) Soils small positive NDVI values (0.1-0.2). free standing water - very low positive or even slightly negative NDVI values clouds and snow fields - negative values of NDVI AVHRR 1 AVHRR 1

NDVI in Europe. Source: DLR

Transformed Vegetation Index TVI TVI TM4 TM4 TM3 TM3 0,5 *100 Designed as a modification of NDVI (1975) Express the quantity of green biomass Calibration required Partly eliminates the occurence of negative values linear development (normal distribution of data)

Corrected Transformed Vegetation Index CTVI Proposed as a modification of TVI (1984) Eliminates disadvantages of NDVI and TVI caused by negative values Disadvantage overestimation of the green vegetation occurrence

Thiam s Transformed Vegetation Index TTVI navržen jako úprava CTVI (Thiam, 1999) eliminuje nedostatky CTVI způsobené nadhodnocováním výskytu vegetace

Normalized Ratio Vegetation Index NRVI efekt normalizace se projevuje podobně jako u NDVI minimalizován problém variability osvětlení v důsledku topografických vlivů lineární průběh stupnice omezuje negativní vliv atmosféry normální rozdělení hodnot NRVI

Orthogonal (distance-based) VI využívají linii půd - popisující charakteristické příznaky odrazivosti půd pro R a NIR záření

Orthogonal (distance-based) VI pixely, reprezentující holé půdy, vykazují kladnou korelaci hodnot odrazivosti pro R a NIR pásma výpočet rovnice linie půd metodou nejmenších čtverců

Perpendicular Vegetation Index PVI základ ortogonálních indexů z PVI odvozeny další vegetační indexy hlavní smyslem je: minimalizovat vliv míchání spektrální odezvy řídké vegetace a půd v pozadí řeší problém smíšených pixelů použití v aridních a semi-aridních oblastech

Other (non-vi) indexes

Other (non-vi) indexes NDWI normalised difference water index (vlhkost v rostlinách), vyžadována pásma 900 a 1250 nm NDSI - normalised difference snow index pásma 500 nm a 1500 nm NBR - normalised burn ratio pásma 1000 nm a 2000 nm Hydrokarbonový index (Smejkalová)

Dobrovolný

http://web.natur.cuni.cz/ugp/main/staff/martinek/dpzda ta/3-dpz-imageanal.pdf analýza hlavních komponent, dekorelační roztažení histogramu

http://web.natur.cuni.cz/ugp/main/staff/martinek/dpzda ta/3-dpz-imageanal.pdf PC1, PC2, PC3 jako RGB PC1, PC2, PC3 jako RGB decorrelation a D-roztažení

Dobrovolný

Kolář

http://web.natur.cuni.cz/ugp/main/staff/martinek/dpzda ta/3-dpz-imageanal.pdf RGB-IHS transformace

Dobrovolný

Resolution Merge spojení obrazů s různým rozlišením získání výsledného obrazu, který má prostorové a spektrální rozlišení stejné jako vyšší z obou původních vstupních obrazových záznamů např.: Landsat 7 - pásma 1-5, 7 prostorové rozlišení 30 m/pixel Landsat 7 - pásmo 8 prostorové rozlišení 15 m/pixel výsledkem tzv. PAN sharpened image

Resolution Merge

Jiné varianty využití RGB<->IHS Odlišení jemných změn v plochách hornin: MSS4, MSS5, MSS7 do složky I; MSS5/MSS4 do H; MSS5/MSS6 do S; Pak zpětná transformace do RGB Podobně geologické struktury pro SEASAT Tvorba zavěšených map na reliéfu: Obraz mapy do I; DMT do H; 127 do S; zpětná transformace do RGB Zvýraznění AVHRR (pouze 2 pásma). 1+2 = I; 2/1 do H; 1-2=S

http://web.natur.cuni.cz/ugp/main/staff/martinek/dpzda ta/3-dpz-imageanal.pdf TM 5/4 3/1 5/7 jako RGB - HSI transformace

Brovey transformation využívá 3 pásma multispektrálního obrazového záznamu + obraz s vysokým prostorovým rozlišením např.: [DN B1 / (DN B1 + DN B2 + DN B3 )] x [DN HiResImage ] = DN B1new [DN B2 / (DN B1 + DN B2 + DN B3 )] x [DN HiResImage ] = DN B2new [DN B3 / (DN B1 + DN B2 + DN B3 )] x [DN HiResImage ] = DN B3new usnadnění vizuální interpretace zvýšení kontrastu v oblast nejsvětlejších a nejtmavších charakteristik (poskytuje kontrast ve stínech, v oblasti vodních objektů, )

Dobrovolný

M-T Transformation merging principal components využívá PC-1 pásmo iluminace scény usnadnění vizuální interpretace zvýšení kontrastu v oblast nejsvětlejších a nejtmavších charakteristik (poskytuje kontrast ve stínech, v oblasti vodních objektů, )

Literatura Jackson, R. D. and Huete, A. R. (1991) Interpreting vegetation indexes. Prev. Vet. Med. 11. pp. 185 200 Dobrovolný P.: Dálkový průzkum Země. Digitální zpracování obrazu. Brno 1998. http://www.chemagazin.cz/userdata/chemagazin_2010/file/che MAGAZIN_XX_6_cl10%281%29.pdf IDRISI Manual, kap. 18 Vegetation indices