FUNDAMENTALS OF DIGITAL IMAGES

Transcription

1 FUNDAMENTALS OF DIGITAL IMAGES Lecture

2 Image Data Structures Common Data Structures to Store Multiband Data BIL band interleaved by line BSQ band sequential BIP band interleaved by pixel

3 Example Band Band Band bands, 9 pixels each in (x format)

4 BIL 4 Band interleaved by line storage format MxN Image; K Bands; One row on ground B B B N B B B N B k B k B kn A single file on disk or CD contains M.K rows, each having N columns; Every K rows in the file correspond to ONE ROW ON THE GROUND

5 BIL 5 BIL FILE STRUCTURE Band Row Band K Row Band Row Band K Row Band Row M Band K Row M Image Size M rows N columns K Bands

6 6 Line #, band # is stored first Followed by line #, band # Bands are inter-leaved by line BIL format

7 BIL 7 BIL is a popular format for storing multispectral images, and supported by most remote sensing software (ERDAS, PCI, ) Well suited when multiband data analysis is required Lot of data I/O involved when access to a single band image is needed on sequential access systems. Moderate overhead on random access systems

8 BSQ 8 Band sequential method involves storing one full single band image after another B B B N B B B N B M B M B MN The image for the second band,, up to Band K follow

9 BSQ 9 Image Size M rows N columns K Bands Band Row Band Row M Band Row Band Row M Band K Row Band K Row M Band Band Band K

10 0 Band # is stored first Followed by #, # Bands are stored sequentially Band sequential (BSQ) format

11 BSQ Ideally suited when the multiband image is processed one band at a time, such as image enhancement, neighbourhood filtering, etc. More overheads when all band values are required at each pixel

12 BIP Band interleaved by pixel Commonly used for storing color images, with red, green and blue values alternating R G B R G B R G B Not used in present times to store satellite images Used in the early stages of Landsat data distribution

13 BIP First Row Band Band Band K Band Band Band K Band K Row Row Row Row Row Row Row Pixel Pixel Pixel Pixel Pixel Pixel Pixel N Band Band Band K Band Band Band K Band K Row Row Row Row Row Row Row Pixel Pixel Pixel Pixel Pixel Pixel Pixel N Second Row M th Row Band Band Band K Band Band Band K Band K Row M Row M Row M Row M Row M Row M Row M Pixel Pixel Pixel Pixel Pixel Pixel Pixel N

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15 Disk File Size of the image 5 Rows x Cols x Bands x Bytes per pixel For the SPOT window, 500 x 500 x x = bytes ~ 750 KB In case of Ikonos image, storage is bytes per pixel, 4 metres resolution, 4 bands 0 km x 0 km Ikonos multispectral image size on disk = 0000/4 x 0000/4 x 4 x = 0000 x 5000 bytes ~ 50 MB Size of panchromatic image = 0000 x 0000 x = 0000 x 0000 bytes ~00 MB NOTE THE DIFFERENCE IN SIZE OF DATA!

16 Spectral bands and Spatial Resolution 6 Spatial resolution is highest for panchromatic images Lower for multispectral images Reason? In case of multispectral sensors, received energy is divided into band-wise slices; hence lesser amount of energy to detectors Compensated by increasing time of observing ground features hence lower spatial resolution

17 Image Sensing and Acquisition 7

18 Image Formation Model 8

19 Image Sampling & Quantization 9

20 Image Sampling & Quantization 0

21 Image Sampling & Quantization Sampling: Digitizing the coordinate values (spatial resolution) Quantization: Digitizing the amplitude values (intensity levels)

22 Image Quantization

23 Image Sampling

24 Image Sampling 4

25 Image Sampling 5

26 Image Sampling 6

27 Image Sampling 7 Original 56 x56 8 x 8

28 Image Sampling 8 Original 56 x56 64 x 64

29 Image Sampling 9 Original 56 x56 x

30 Digital Image Representation 0

31 Downsampling

32 Downsampling

33 Re-Sampling

34 Grey Level Quantization 4

35 Grey Level Quantization 5 Original 56 64

36 Grey Level Quantization 6 Original 56 6

37 Grey Level Quantization 7 Original 56 4

38 Grey Level Quantization 8 Original 56

39 Digital Image Representation 9

40 Digital Image Representation 40

41 Basic relationships between pixels 4

42 Basic relationships between pixels 4

43 Basic relationships between pixels 4

44 Basic relationships between pixels 44

45 Basic relationships between pixels 45

46 Set Logic Operations 46

47 Distance Function 47

48 Distance Function 48

49 Distance Function 49

50 Distance Function Examples 50