Introduction to image processing for remote sensing: Practical examples

Transcription

1 Università degli studi di Roma Tor Vergata Corso di Telerilevamento e Diagnostica Elettromagnetica Anno accademico 2010/2011 Introduction to image processing for remote sensing: Practical examples Dr. Matteo Picchiani picchiani@disp.uniroma2.it 07/06/2011

2 ESA EO User Toolboxes ESA has developed a series of Software Toolboxes Each Toolbox is a collection of OPEN SOURCE software tools to help the remote sensing community to exploit ESA (and others company) data. BASIC ERS&ENVISAT (A)ATSR AND MERIS TOOLBOX (BEAM) BASIC ERS&ENVISAT ATMOSPHERIC TOOLBOX (BEAT) BASIC RADAR ALTIMETER TOOLBOX (BEAT) POLARIMETRIC SAR DATA PROCESSING AND EDUCATIONAL TOOL (POLSARPRO) NEXT ESA SAR TOOLBOX (NEST) GOCE USER TOOLBOX (GUT)

3 BASIC ERS&ENVISAT(A)ATSR AND MERIS TOOLBOXis BEAM is an open-source Java toolbox and development platform for viewing, analyzing and processing of optical remote sensing raster data. BEAM supports different ESA and other EO sensors and generic data formats. Supported Instruments Generic EO Data Formats From

4 BEAM Components VISAT - An intuitive desktop application to be used for visualization, analyzing and processing of remote sensing raster data. A set of scientific tools (> 11 Data Processors) running either from the command line or invoked by VISAT, also entirely written in Java. A rich Java API for the development of new remote sensing applications and BEAM extension plug-ins. User support: Tutorials, Plug-Ins, Issue tracker, Community Wiki ( From

5 VISAT Image display and navigation even of giga-pixel images Layer management allows adding and manipulation of overlays such as other bands, images from WMS servers or ESRI shapefiles Region-of-interest definitions for statistics and various plots Band arithmetic using arbitrary mathematical expressions Reprojection and ortho-rectification to common map projections Geo-coding and rectification using ground control points Store and restore the current session including all opened files, views and layers From

6 VISAT INTERFACE Menu Bar Product View Tool Bar Image View

7 Satellite raster data processing Enhancing an image or extracting information or features from an image. Computerized routines for information extraction (eg, pattern recognition, classification) from remotely sensed images to obtain categories of information about specific features.

8 Image Processing Includes Image enhancement and sharpening Image filtering Radiometric correction Geometric correction Image classification Pixel based Object-oriented based Post-classification and GIS Change detection

9 What Is Image Enhancement? Image enhancement is the process of making images more useful. The reasons for doing this include: Highlighting interesting detail in images. Change the visualization in order to achieve information. Making images more visually appealing.

10 Data Opening:

11 Data Opening:

12 Data Resizing:

13 Data Resizing:

14 Data Resizing:

15 Linear Enhancement

16 Linear Enhancement

17 Linear Enhancement

18 Non-linear Enhancement Are enhancement techniques that stretches the range of image brightness in a non-proportional manner. A nonlinear stretch expands one portion of the grey scale while compressing the other portion. While spatial information is preserved, quantitative radiometric information can be lost. E.g. a logarithmic stretch compresses the higher brightness values within an image and disproportionately expands the darker values. A - Unity Transfer Function B - Logarithmic Transfer Function C - Root Transfer Function D - Exponential Transfer Function E - Inverse Logarithmic Transfer Function F - Histogram Equalization Transfer Function DN - original brightness values DN' - stretched brightness values

19 Non-linear Enhancement

20 Non-linear Enhancement

21 Non-linear Enhancement

22

23 Spatial Filtering

24 Spatial Filtering

25 Low pass 3x3 Spatial Filtering

26 Low pass 5x5 Spatial Filtering

27 High pass 3x3 Spatial Filtering

28 High pass 5x5 Spatial Filtering

29 Spatial Filtering

30 Laplace 3x3 Spatial Filtering

31 High pass 3x3 Laplace 3x3 Spatial Filtering

32 False Colour Composites

33 Remote sensing images are taken within specific spectral regions. Electromagnetic Spectrum

34 The spectral bands of the LANDSAT Thematic Mapper LandSat TM produces 7 digital images of a scene representing the 7 bands of electromagnetic energy captured

35 False Colour Composite Steps:

36 * DATA FILE NAMES The file naming convention for Landsat 7 GeoTIFF is as follows: L7fppprrr_rrrYYYYMMDD_AAA.TIF where: L7 = Landsat-7 mission f = ETM+ data format (1 or 2) ppp = starting path of the product rrr_rrr = starting and ending rows of the product YYYYMMDD = acquisition date of the image AAA = file type: B10 = band 1 B20 = band 2 B30 = band 3 B40 = band 4 B50 = band 5 B61 = band 6L (low gain) B62 = band 6H (high gain) B70 = band 7 B80 = band 8 MTL = Level-1 metadata TIF = GeoTIFF file extension

37 False Colour Composite Steps:

38 False Colour Composite Steps:

39 False Colour Composite Steps:

40 False Colour Composite Steps:

41 Band ratioing Ratioing is an enhancement process in which the DN value of one band is divided by that of any other band in the sensor array. If both values are similar, the resulting quotient is a number close to 1. If the numerator number is low and denominator high, the quotient approaches zero. If this is reversed (high numerator; low denominator) the number is well above 1. These new numbers can be stretched or expanded to produce images with considerable contrast variation in a black and white rendition. Certain features or materials can produce distinctive gray tones in certain ratios.

42 TM4/TM3: This ratio distinguished vegetation, water and croplands. It has enhanced forests, barren lands. Because forests or vegetation exhibits higher reflectance in near IR region ( um) and strong absorption in red region ( u m) region. This ratio uniquely defines the distribution of vegetation. The lighter the tone, the greater the amount of vegetation present.

43 TM4/TM3:

44 TM4/TM3:

45

46 Normalized Difference Vegetation Index (NDVI): This is a commonly use vegetation index which uses the red and infrared bands of the EM spectrum. NDVI = (NIR-Red)/(NIR+Red)

47 Normalized Difference Vegetation Index (NDVI):

48

49 Display a histogram of a vegetated area

50 ROI Selection

51 Display a histogram of a vegetated area

52 Meris MERIS is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called "push-broom" method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5 field of view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. From :

53 MDS Nr. Band centre (nm) Meris Spectral Bands Bandwidth (nm) Potential Applications Yellow substance, turbidity Chlorophyll absorption maximum Chlorophyll, other pigments Turbidity, suspended sediment, red tides Chlorophyll reference, suspended sediment Suspended sediment Chlorophyll absorption Chlorophyll fluorescence Atmospheric correction, red edge Oxygen absorption reference Oxygen absorption R-branch Aerosols, vegetation Aerosols corrections over ocean Water vapour absorption reference Water vapour absorption, vegetation

54 Meris False Colour Composite:

55 Meris False Colour Composite:

56 Meris False Colour Composite:

57 False Colour Composite BEAM defaults:

58 False Colour Composite BEAM defaults:

59 False Colour Composite BEAM defaults:

60 False Colour Composite BEAM defaults:

61 False Colour Composite BEAM defaults:

62 BEAM default MERIS NDVI function:

63 BEAM default MERIS NDVI function:

64 BEAM default MERIS NDVI function:

65 BEAM default MERIS NDVI function:

66 BEAM default MERIS NDVI function:

67 Use of Masks

68 Use of Masks

69 Use of Masks

70 Use of Masks

71 Use of Masks

72 Use of Masks

73 Use of Masks

74 Use of Masks

75 Use of Masks

76 Use of Masks

77 Use of Masks

78 Use of Masks

79 Use of Masks

80 Use of Masks

81 Use of Masks

82 Use of Masks

83 Meris cloud probability processor

84 Meris cloud probability processor

85 Image reprojection Map projection: this step allows to go from geographic coordinates to some specific cartographic projection as Lambert, Mercator or UTM. For maps of the Earth, a projection consists of a graticule of lines representing parallels of latitude and meridians of longitude or a grid.

86 Image reprojection

87 Image reprojection

88 Image reprojection

89 Image reprojection

90 Image reprojection

91 Image Orthorectification Orthorectification is the process by which the geometric distortions of the image are modeled and accounted for, resulting in a planimetricly correct image. To put it another way, our 3D world is imaged by most sensors in 2D and orthorectification corrects for many of the anomalies resultant from this conversion. Orthorectified imagery is particularly useful in areas of the world with exacerbated terrain features such as mountains, plateaus, etc. The orthorectification process yields map-accurate images which can be highly useful as base maps and may be easily incorporated into a GIS. The success of the orthorectification process depends on the accuracy of the DEM and the correction formulae. In the case of the data provided by GLCF, the most accurate publicly available DEM was used and an RMS error of 50 meters or better can be expected.

92 Image Orthorectification

93 Image Orthorectification

94 Image Orthorectification

95 Image Orthorectification