Satellite Image Analysis

Transcription

1 Satellite Image Analysis Chistian Melsheime Apil 25, 2012 The lab on satellite image analysis deals with a vey typical application, the extaction of land use infomation. Stating point is an image ecoded by a satellite, which is then analysed in ode to extact infomation on the type of gound cove (e.g., vegetation, wate, uban aea). The following gives a vey bief summay of concept necessay fo this lab expeiment. Some topics will be teated late in othe lectues as well. As the title implies, thee ae 3 topics to deal with: Satellites: How fast/high? Whee? When? Obits, height,... Images: Which wavelength? What instument? Digital? How to display?... Analysis: How to distinguish, e.g, foest fom wate? Automatically?... 1 Satellites 1.1 Cicula obit satellite: mass m, obit height h, obit adius, speed v, angula velocity ω = v/ 1

2 foce of gavity: ( ) 2 RE F g = mg (1) whee g = 9.81 m/s 2 is the acceleation of gavity at the Eath suface centipetal foce (fo cicula obit): cicula obit: F g = F c mg F c = mω 2 = mv2 ( RE ) 2 = v2 v = Note: No dependence on satellite mass! gr E 2 (2) (3) obital peiod: T = 2π ω = 2π v = 2π gr 2 E (4) Example: geostationay obit (communication satellites): T = 23h56min = [T gr E /(2π)] 2/3 h = R E =... = km. Example: many emote sensing satellites: h 800 km v = 7.46 km/s, T 101min. Note: T dictates h and vice vesa 1.2 Sun-synchonous obit Inclination I = angle between equato plane and obital plane 2

3 equato-cossing South-Noth = ascending node Eath not spheical (gavitation potential = 1 + highe ode tems) pecession of obit if obit not in equato plane Pecession fequency = function of I and fo I = 98, pecession peiod is 1 yea! i.e., position of obit elative to sun is constant satellite passes ove same latitude at the same local time this is called sun-synchonous obit useful to avoid vaying illumination conditions caused by diffeent time of day used by most emote-sensing satellites Note: seasonal vaiation of illumination cannot be avoided: Appoximate sun elevation angle at local noon fo some locations and the 4 seasons 21 Dec 21 Ma/22 Sep 21 Jun Bemen, 53 N Delhi, 28 N Singapoe, 1 N (ove S hoizon) (zenith) (ove N hoizon) gound tack of sun-synchonous, nea pola obiting satellite: wavy line 3

4 epeat passes: Afte a numbe of obits, sub-satellite point etaces its path epeat cycle. 2 Images 2.1 Satellite sensos active sensos: senso poduces its own illumination of object (e.g., Rada, Lida) passive sensos: senso uses adiation emitted/eflected/scatteed by object (e.g., Radiometes) wavelength anges used in emote sensing of the Eath: name wavelength ange adiation souce suface popety of inteest Visible µm sola eflectance Nea Infaed µm sola eflectance (NIR) Shot Wave µm sola eflectance Infaed µm (SWIR) µm Mid Infaed 3-4 µm sola,themal eflectance, (MWIR) µm sola,themal tempeatue Themal µm themal tempeatue Infaed (TIR) µm micowave, ada 1 mm 1 m themal (passive) tempeatue (passive) atificial (active) oughness(active) 4

5 Fom now on: only visible/nir passive senso (the type elevant fo the satellite image analysis expeiment) senso measues adiance, i.e., intensity of adiation constucts 2-dimensional image fom many point measuements (=digital image) uses movement of satellite (see Figue 1), scans coss-tack, o has an aay of detectos/sensos Figue 1: Scanning of the gound by a satellite 5

6 IFOV = instantaneous field of view: angle, size depends on instument (optics) and wavelength gound-pojected IFOV (GIFOV) depends on IFOV and height; dimension: length GIFOV spatial esolution (minimum distance two small objects must have to be imaged sepaately) Spatial esolution of visible/nir satellites: 1 m 1000 m Multi-spectal sensos: seveal fequency anges at once, e.g., blue, geen, ed, NIR (4 spectal channels ) hypespectal sensos: dozens to hundeds of spectal channels 2.2 Digital images Definition: Digital image 2-dimensional aay (=matix) of adiance measuements (=digital numbes) = pixels typical: (seveal hundeds to seveal thousands) 2 pixels e.g., pixels, stoed on compute as 1300 lines with 1300 digital numbes each typical: bit (=1 byte) pe pixel, i.e., 256 diffeent pixel values: displayed as gey levels, by convention usually 0=black and 255=white, also called half-tone image (black, gey shades, white) Histogam, Contast enhancement gey-level histogam shows the fequency of occuence of each gey level histogam shows if all possible gey levels ae used, i.e. if thee is good contast 6

7 gey levels can be tansfomed to enhance contast Example: dakest pixel has value D = 100 and bightest D = 151 linea tansfom D new = 5(D 100) tansfoms 100 to 0 and 151 to 255, stetching the contast. Colou images needs image with 3 channels which ae displayed in ed (R), geen (G) and blue (B) RGB colou. why RGB? Human eye has 3 types of colou eceptos ( cones ), sensitive to ed, geen, and blue light (appoximately) Result: Tue colou o false colou image: display senso spectal band colou Tue Colou Colou IR False Colou ed (R) ed NIR any geen (G) geen ed any blue (B) blue geen any Note: A single-channel image can also be displayed as colou by using a ange of colous ( palette, colou table ) instead of the gey levels this is called pseudo-colou 3 Image Analysis 3.1 Scatteing/Reflection in Visible/NIR Diffeent mateials eflect/scatte diffeent wavelengths diffeently In othe wods: Diffeent mateials have diffeent spectal signatues Most impotant fact hee: stong eflection of NIR by vegetation: 7

8 suface stuctue (on the wavelength scale) mattes as well: smooth sufaces (e.g., wate suface 1 ) cause specula eflection: ough sufaces (e.g., soil, gass, oads) cause diffuse eflection: distibuted scattees (e.g., leaves, twigs, banches in foest) cause multiple scatteing, sometimes called volume scatteing 3.2 Suface type classification use the diffeence in spectal signatues fo distinguishing suface types Vegetation density is a decisive featue of most suface types: e.g., fields and uban aeas can be distinguished mainly by thee diffeent 1 Note that a potion of light at the wate suface is also efacted into the wate and is scatteed and/o absobed thee 8

9 vegetation density Using just the ed channel and the NIR channel, a lot can be done: Plot pixels in a ed-nir scatte plot accoding to thei pixel values in these channels: pixels epesenting diffeent suface types often goup into distinct clouds ( clustes ) Semi-automatic classification algoithm (athe simplistic, but woking): 1. select aeas whose type is known ( taining aeas ), e.g., fom a map; one taining aea fo each suface type 2. display ed-nir scatte plot, and get the anges in ed and NIR that the cluste coesponding to each suface type occupies 3. classify pixels fom unknown aeas accoding to the anges fom pevious step This is a so-called paallelepiped 2 classifie 2 a kind of N-dim. box 9