Remote Sensing & Photogrammetry L6. Beata Hejmanowska Building C4, room 212, phone:

Transkrypt

1 Remote Sensing & Photogrammetry L6 Beata Hejmanowska Building C4, room 212, phone:

2 Image processing 1. Visual interpretation of single spectral band Readout of DN and coordinates: x,y 2. Image enhancement Histogram calculation Linear histogram streatching Image comparison before and after stretching Different parameters of linear stretching Histogram saturation and equalization 3. Visualization of multispectral bands RGB FCC

3 Image processing 4. Multi bands operation Ratio Vegetation Index (VI) Normalized ratio Normalized Vegetation Index (NDVI) Multi chanel statistics Principal Componemt Analysis (PCA) (presented live Lecture 5) Map algebra Image fusion 5. Image classification Density slicing piece-wise linear stretchning Mulispectral image classification Sampling Different algorithms Classification accuracy assessement Post classification operation

4 Vegetation index Ratio Vegetation Index VI=NIR/RED NDVI Normalized Difference Vegetation Index NDVI=(NIR-RED)/(NIR+RED) KB 234 TM2 N TM3 VI= TM4 / TM3 TM4 Wykłady TiF II -2010/11 Regina Tokarczyk

5 NDVI Normalized Differential Vegetation Index NDVI=(TM4-TM3)/(TM4+TM3)

6 Normalized Burn Ratio Normalized Burn Ratio NBR = (TM4 - TM7) / (TM4 + TM7) Normalized Difference Burn Ratio (NDBR) NDBR = NBR pre NBR post Wykłady TiF II -2010/11 Regina Tokarczyk

7 SI radiometry units

8 Irradiance Irradinace ratio of incidient radiant flux on elementar unit flux stosunek strumienia padającego w elementarnej jednostce czasu na element powierzchni odbiornika do wielkości tej powierzchni dla wąskiego przedziału spektralnego p.e.m. (emitancja spektralna) E = dq/dt/da=φ/da W/m 2 Constant on the top of atmosphere : mean value 1366,1 W/(m 2 ) solar constant E λ = dq /dt/da/ kąt bryłowy W/(m 2 srµm)

9 Albedo Albedo (reflectance coefficient) = Energy reflected from the Earth/ Energy incident on the Earth = Q ρλ /Q =L λ λ /E λ (in all EM range) ρλ Albedo depends on: -Enegry incident (solar zenith angle) on horizontal and sloping surfaces -Reflecting object surface (color, charakter, roughness, wetness) Lambert s law: Energy incident on the Earth = Energy incident on the Earth (vertical) * cos (Vo) - Vo local zenith angle Example albedo: Soil 5-10% Conifer forest 15-20% Grass 20-25% Snow: fresh 75-95%, old 50-60% antopological materials ( 5-20% asphalt, 10-35% concrete, 20-35% stone, 10-35% tile

10 Map algebra - radiance Radiance (luminance) calculation: (( Lmax Lmin )/ 255) DN Lmin L = gain DN + offset = + λ L λ spectral radinace recorded by sensor L min minimal radiance of detectors in PAN: 5.00 L max maximal radiance of detectors in PAN: DN of channel 8, PAN

11 Map algebra - albedo Albedo ρ = π ESUN L λ λ d 2 cosθ S where: ρ albedo L λ spectral radiance recorded by sensor d distance between Earth and Sun in astronomical units for given day of the year ESUN λ mean irradinace = θ S Sun zenith angle

12 Map algebra - albedo DN PAN Band 8 Landsat ETM+ Albedo ( µm)

13 VIR, SWIR, TIR Spectral curves 60% 50% gleba 40% T 30% 20% 10% wody 0 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 2,0 2,2 2,4 B G R bliska podczerwień średnia podczerwień roślinność długość fali um krzywa spektralna - las liściasty krzywa spektralna - las liściasty Krzywa spektralna - woda krzywa spektralna - woda współczynnik odbicia [%] długość fali [mikrometr] współczynnik emisyjności [-] długość fali [mikrometr] współczynnik odbicia [%] długość fali [mikrometr] współczynnik emisyjności [-] długość fali [mikrometr] E ρ (λ) =ρ (λ) E (λ) E s (λ) = ε (λ) B(λ,T s ) + (1-ε (λ)) E (λ)

14 EM laws TIR

15 EM laws TIR E=εσT 4 σ - Stefan-Boltzmann constant, 5, W /( m 2 K)

16 Map algebra - temperature Radiant temperature T = 4 ε T rzecz Ε= στ 4 =σ[(ε) 1/4 Τ rzecz ] 4 =σετ 4 rzecz Temperatura radiacyjna (0.06) 1/ K = o C Temperatura radiacyjna (0.97) 1/ Κ = +8 ο Χ Powierzchnia wypolerowana wsp. emisyjności 0.06 Powierzchnia pomalowana wsp. emisyjności 0.97 Aluminium kinetic temerature 10 0 C +273= K

17 Radiant temperature Wykłady TiF II -2010/11 Regina Tokarczyk

18 where: L λ spectral radiance of the sensor, L min minimum spectral radiance, in thermal band 0.00, L max maximum spectral radiance, in thermal band DN of thermal band (( Lmax Lmin )/ 255) DN Lmin L = gain DN + offset = + λ Map algebra - temperature Temperature ( W m sr µ m )

19 Map algebra - temperature DN band 6 Landsat ETM+ Radiant temperature

20 Image processing 4. Multi bands operation Ratio Vegetation Index (VI) Normalized ratio Normalized Vegetation Index (NDVI) Multi chanel statistics Principal Componemt Analysis (PCA) (presented live Lecture 5) Map algebra Image fusion 5. Image classification Density slicing piece-wise linear stretchning Mulispectral image classification Sampling Different algorithms Classification accuracy assessement Post classification operation

21 Image fusion + = Multispectral image composition low spatial resolution PAN High spatial resoluion merging, band sharpening

22 Image fusion IHS TRANSFORMATION KB 123 H I S

23 Image fusion IHS TRANSFORMATION KB 123 H I S

24 123 Image fusion

25 174 Image fusion