Aardobservatie en Data-analyse Image processing

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Aardobservatie en Data-analyse Image processing 1 Image processing: Processing

image calibration: DN into radiance or into reflectance - extracting information

improvement/enhancement for visual interpretation for image classification -

1 Aardobservatie en Data-analyse Image processing 1 Image processing: Processing of digital images aiming at: - image correction (geometry, dropped lines, etc) - image calibration: DN into radiance or into reflectance - extracting information : identifying objects, mapping object properties, - image improvement/enhancement for visual interpretation for image classification - time series analyses to detect change Includes processing in the spectral, spatial and/or temporal domain 2 1

Raw satellite image (DN) Radiometric correction Geometric correction preprocessing Topographic Map Ground control points Data reduction Image enhancement processing Objective image analysis

2 Raw satellite image (DN) Radiometric correction Geometric correction preprocessing Topographic Map Ground control points Data reduction Image enhancement processing Objective image analysis Classification / interpretation Final product: RS image GI-system classification Data sets Maps, Change maps 3 Image processing software systems: Erdas/Imagine (UU,WU,ITC,DHV,RWS,ALTERA) IDL/ENVI: focus on hyperspectral RS (UU, WUR, ITC) ecognition: object based image processing ArcInfo/Arcview: image analyst Context Vision (euroconsult, ITC) I 2 S (eurosense) RESEDA (NLR) PCI Low budget software: Idrisi Micropips Ilwis(ITC) 4 2

levels, 0-255 MSS: 6 bits: 64 NOAA: 10 bits, 1024 5

3 Digital Image Format Quantization Number of grey values per pixel Digital Number TM: 8 bits, 256 levels, MSS: 6 bits: 64 NOAA: 10 bits, Construction of a digital image after scanning (TM & MSS): 6 3

4 Traditional way of visualizing remote sensing images : 'Density prints' (matrix printer) - grey tones by printing characters over and through each other - rectangle-shaped pixels Image Histogram Source: UU Remote Sensing practical, Spatial Resolution / Pixel size: 8 4

5 Field pictures of De Blauwe Kamer Low spatial resolution High spatial resolution 9 Effect of low spatial resolution for some land cover types origin of mixed pixels or mixels 10 5

Spectral resolution 11 Format of Digital Remote Sensing Images 1 2 3 Image

Spectral separation of reflection Spectral Signature 0.

6 Spectral resolution 11 Format of Digital Remote Sensing Images Image construction: 1. scan lines 2. Pixels on each scan line 3. Spectral separation of reflection Spectral Signature 0.55 Matrices with spectral info Reflectance Wavelength (nm) Res.: 1.5 nm Res.: TM 12 6

bands in X Y - space % reflectance 870 nm Vegetation Green

7 Presenting image information Histogram 13 Presenting image information Feature Space Combinations of two spectral bands in X Y - space % reflectance 870 nm Vegetation Green senescent bright Soils dark Shade/water % reflectance 650 nm 14 7

4 methods of looking at a RS image: Histogram, Feature space, Image space, Spectral signature 0.55 0.45 Reflectance 0.35 0.25 0.15 0.05-0.05 0 400 800 1200 1600 2000 2400 2800 Wavelength (nm) 15 Res.

8 4 methods of looking at a RS image: Histogram, Feature space, Image space, Spectral signature Reflectance Wavelength (nm) 15 Res.: 1.5 nm Res.: TM Spectral signatures the basis for mapping using RS Function of spatial resolution, spectral resolution, time of data acquistion Bauxite 2. Dolomite 3. Buxus 4. YelBarley 5. Limestone Reflectance Wavelength (nm) 16 8

9 Image stretching to assist visual interpretation 17 IKONOS XS Uithof without stretch: after stretch: 18 9

10 Raw remote sensing images delivered in DN: DN: Digital Numbers Two physical units for radiance: 1. Radiance: expressed in W/m 2 /ster/μm. STER: steradian or solid angle: flux density in 3 D space Ω = A s / r 2 2. Reflectance: ratio between incoming radiance (sun) and the radiance registered by the sensor 19 Image calibration from DN to radiance to reflectance: sensor sensitivity (DN into radiance): L (λ) = G DN + B radiance into reflectance: πl( λ) R( λ ) = 2 E o ( λ)(1/ r ) cos( θ ) o E o : is the solar constant in the bandpass of the sensor r : is the normalized Earth - Sun distance (in astronomical units ~ 1.0); θ o : is the solar zenith angle at the image centre (i.e. seasonal position of the sun); pi (π): ; DN G B is the digital count (DN) in the specific spectral sensor band; is the calibration slope for the specific sensor band (channel gain); is the calibration offset for zero radiance for that sensor band (bias, channel offset) P493 L&K 20 10

11 Solar constant for image calibration: 21 Image classification From many spectral layers to thematic information 22 11

Concept of Image Classification: Multi-Spectral Image

Sample Spectral Pattern of training sites 2.

Assign pixel to most similar category Output: Thematic Raster

12 Concept of Image Classification: Multi-Spectral Image Training Set: Digitize Polygons Band # Band 3 Band 2 Band 1 1. Sample Spectral Pattern of training sites 2. Compare unknown pixel to patterns 3. Assign pixel to most similar category Output: Thematic Raster Map 23 Supervised and unsupervised image classification Controlled by image analyst Statistial clustering 24 12

Digitizing ground truth polygons on-screen 25 Image Space and Feature Space Flight direction Digital numbers band 2 Scanrichting 21 24 30 40 25 6 35 26 28 33 28 8 28

13 Digitizing ground truth polygons on-screen 25 Image Space and Feature Space Flight direction Digital numbers band 2 Scanrichting Digital numbers band 1 Image space Spatial patterns Feature space spectral patterns 26 13

14 Flight direction Digital numbers band 2 Image Space and Feature Space Scan direction B A C Digital numbers band 1 Image space Spatial patterns Feature space spectral patterns 27 Minimum distance to mean classifier Mean & Euclidean distance Digital numbers band water stad/urban bos/forest gewas/crop bodem/soil heide/moor Digital numbers band

15 Parallelepiped or box classifier Range of values Digital numbers band water stad/urban bos/forest gewas/crop bodem/soil heide/moor Digital numbers band 4 29 Maximum likelihood classifier Mean & standard deviation Digital numbers band water stad/urban bos/forest gewas/crop bodem/soil heide/moor Digital numbers band

16 Compare decision rules: minimum distance to mean maximum likelihood 31 Land use classification NL (LGN) 32 Wageningen UR

basis Reports also categories of mis-classifications

17 The Error Matrix (Confusion Matrix) Standard tool for reporting error Produced on a pixel-by-pixel basis Reports also categories of mis-classifications Produced on the basis of reference map 33 Error matrix 34 17

18 User s and Producer s accuracy Producer s accuracy: How well are observations represented in map? User s accuracy: How reliable is the map? What is the change that the map represents reality? 35 Error matrix map Producer s accuracy 150/225=0.67 reality 730/973= /594= /126= /483= /191=0.60 User s accuracy 150/ / /451 83/ / /

19 Examples of compatible and incompatible classes Compatible Classes: Reference map Water Urban Urban Urban Decid. forest Conif. forest Mixed forest RS image Water Urban residential Urban commercial Urban industrial Forest Forest Forest Incompatible Classes: Reference map RS image Open land Crop land Water Lakes, rivers Roads, highways

The Gain setting for Landsat 7 (High or Low Gain) depends on: Sensor Calibration - Application. the surface cover types of the earth and the sun angle

The Gain setting for Landsat 7 (High or Low Gain) depends on: Sensor Calibration - Application. the surface cover types of the earth and the sun angle Sensor Calibration - Application Station Identifier ASN Scene Center atitude 34.840 (34 3'0.64"N) Day Night DAY Scene Center ongitude 33.03270 (33 0'7.72"E) WRS Path WRS Row 76 036 Corner Upper eft atitude