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The amount of electromagnetic radiance, The amount of electromagnetic radiance, LL (watts m (watts m-2-2 sr sr-1-1; watts per meter squared ; watts per meter squared per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a picture element in a digital image) is a function of:picture element in a digital image) is a function of:
where, where,
= wavelength (spectral response measured in various bands or at specific = wavelength (spectral response measured in various bands or at specific frequencies). frequencies). ssx,y,zx,y,z = = x, y, zx, y, z location of the picture element and its size ( location of the picture element and its size (x, yx, y), ), tt
= temporal information, i.e., when and how often the information was = temporal information, i.e., when and how often the information was acquired, acquired, = set of angles that describe the geometric relationships among = set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing system. field), and the remote sensing system. PP = polarization of back-scattered = polarization of back-scattered energy recorded by the sensor, energy recorded by the sensor, = radiometric resolution (precision) at = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.recorded by the remote sensing system.
The amount of electromagnetic radiance, The amount of electromagnetic radiance, LL (watts m (watts m-2-2 sr sr-1-1; watts per meter squared ; watts per meter squared per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a picture element in a digital image) is a function of:picture element in a digital image) is a function of:
where, where,
= wavelength (spectral response measured in various bands or at specific = wavelength (spectral response measured in various bands or at specific frequencies). frequencies). ssx,y,zx,y,z = = x, y, zx, y, z location of the picture element and its size ( location of the picture element and its size (x, yx, y), ), tt
= temporal information, i.e., when and how often the information was = temporal information, i.e., when and how often the information was acquired, acquired, = set of angles that describe the geometric relationships among = set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing system. field), and the remote sensing system. PP = polarization of back-scattered = polarization of back-scattered energy recorded by the sensor, energy recorded by the sensor, = radiometric resolution (precision) at = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.recorded by the remote sensing system.
,,,,, ,, PtsfL zyx
Remote Sensing Data CollectionRemote Sensing Data CollectionRemote Sensing Data CollectionRemote Sensing Data Collection
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ssx,y,zx,y,z = = x, y, zx, y, z location of the picture element and its size ( location of the picture element and its size (x, yx, y) )
tt = temporal information, i.e., when and how often the = temporal information, i.e., when and how often the information was acquiredinformation was acquired
= set of angles that describe the geometric relationships = set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing systeminterest (e.g., a corn field), and the remote sensing system
PP = polarization of back-scattered energy recorded by the = polarization of back-scattered energy recorded by the sensorsensor
= radiometric resolution (precision) at which the data (e.g., = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.the remote sensing system.
ssx,y,zx,y,z = = x, y, zx, y, z location of the picture element and its size ( location of the picture element and its size (x, yx, y) )
tt = temporal information, i.e., when and how often the = temporal information, i.e., when and how often the information was acquiredinformation was acquired
= set of angles that describe the geometric relationships = set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing systeminterest (e.g., a corn field), and the remote sensing system
PP = polarization of back-scattered energy recorded by the = polarization of back-scattered energy recorded by the sensorsensor
= radiometric resolution (precision) at which the data (e.g., = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.the remote sensing system.
Remote Sensing Data CollectionRemote Sensing Data CollectionRemote Sensing Data CollectionRemote Sensing Data Collection
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Platforms• Geostationary
Satellites at very high altitudes in which view the same portion of the Earth's surface at all times have geostationary orbits.
They have speeds which match the rotation of the Earth so they seem stationary, relative to the Earth's surface. This allows the satellites to observe and collect information continuously over specific areas.
Altitudes aprox. 36,000 kilometers
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Platforms• Polar Orbit
Altitudes aprox. 800 kilometres
Many of these satellite orbits are also sun-synchronous such that they cover each area of the world at a constant local time of day called local sun time. At any given latitude, the position of the sun in the sky as the satellite passes overhead will be the same within the same season.
Follow an orbit (basically north-south) which, in conjunction with the Earth's rotation (west-east), allows them to cover most of the Earth's surface over a certain period of time.
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Satellite Swath
• The area of the earth which is imaged during a satellite orbit is referred to as the satellite swath and can range in width from ten to hundreds of kilometers.
http://hosting.soonet.ca/eliris/remotesensing/bl130lec11.html
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Instantaneous Field of View (IFOV)
• The IFOV is the angular cone of visibility of the sensor (A) and determines the area on the Earth's surface which is "seen" from a given altitude at one particular moment in time (B).
• The size of the area viewed is determined by multiplying the IFOV by the distance from the ground to the sensor (C). This area on the ground is called the resolution cell and determines a sensor's maximum spatial resolution
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DIGITAL IMAGE
A photograph could also be represented and displayed in adigital format by subdividing the image into small equal-sized andshaped areas, called picture elements or pixels, and representing the brightness of each area with a numeric value or digital number.
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Digital Image
0 128 255
Digital Number
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Remote Sensor ResolutionRemote Sensor ResolutionRemote Sensor ResolutionRemote Sensor Resolution
• • SpatialSpatial - the size of the field-of-view, e.g. 10 x 10 m. - the size of the field-of-view, e.g. 10 x 10 m.
• • SpectralSpectral - the number and size of spectral regions the sensor - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave (radar).thermal infrared, microwave (radar).
• • TemporalTemporal - how often the sensor acquires data, e.g. every 30 days. - how often the sensor acquires data, e.g. every 30 days.
• • RadiometricRadiometric - the sensitivity of detectors to small differences in - the sensitivity of detectors to small differences in
electromagnetic energy. electromagnetic energy.
• • SpatialSpatial - the size of the field-of-view, e.g. 10 x 10 m. - the size of the field-of-view, e.g. 10 x 10 m.
• • SpectralSpectral - the number and size of spectral regions the sensor - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave (radar).thermal infrared, microwave (radar).
• • TemporalTemporal - how often the sensor acquires data, e.g. every 30 days. - how often the sensor acquires data, e.g. every 30 days.
• • RadiometricRadiometric - the sensitivity of detectors to small differences in - the sensitivity of detectors to small differences in
electromagnetic energy. electromagnetic energy.
10 m10 m
BB GG RR NIRNIR
JanJan1515
FebFeb 1515
10 m10 m
Jensen, 2007Jensen, 2007Jensen, 2007Jensen, 2007
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Imagery data are represented by positive digital numbers which vary from 0 to (one less than) a selected power of 2. Each bit records an exponent of power 2 = n bit = 2n
The maximum number of brightness levels available depends on the number of bits used in representing the energy recorded.
1 bit(2 gray tone) 5 bit
(32 gray tone)
Radiometric Resolution
The radiometric resolution of an imaging system describes its ability to discriminate very slight differences in energy. .
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Radiometric ResolutionRadiometric Resolution
Resolução = 2 bits = 22 = 4 níveis de cinza
Resolução = 8 bits = 28 = 256 níveis de cinza
By comparing a 2-bit image with an 8-bit image, we can see that there isa large difference in the level of detail discernible depending on their radiometric resolutions.
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RadiometricRadiometric ResolutionResolutionRadiometricRadiometric ResolutionResolution
8-bit8-bit(0 - 255)(0 - 255)
8-bit8-bit(0 - 255)(0 - 255)
9-bit9-bit(0 - 511)(0 - 511)
9-bit9-bit(0 - 511)(0 - 511)
10-bit10-bit(0 - 1023)(0 - 1023)
10-bit10-bit(0 - 1023)(0 - 1023)
0
0
0
7-bit7-bit(0 - 127)(0 - 127)
7-bit7-bit(0 - 127)(0 - 127)0
Jensen, 2007Jensen, 2007
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Spatial Resolution
The detail discernible in an image is dependent on the spatial resolution of the sensor and refers to the size of the smallest possible feature that can be detected.
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Spatial Spatial ResolutionResolution
Spatial Spatial ResolutionResolution
Jensen, 2007Jensen, 2007
Imagery of residential housing in Mechanicsville, New York, obtained on June 1, 1998, at a nominal spatial resolution of 0.3 x 0.3 m (approximately 1 x 1 ft.) using a digital camera.
Imagery of residential housing in Mechanicsville, New York, obtained on June 1, 1998, at a nominal spatial resolution of 0.3 x 0.3 m (approximately 1 x 1 ft.) using a digital camera.
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Spatial resolutionSpatial resolution
• Sensors
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Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The finer the spectral resolution, the narrower the wavelength range for a particular channel or band.
Spectral ResolutionSpectral Resolution
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Bands• Satellite sensors measure energy from particular set of
wavelengths , which are referred to as “bands” and numbered in increasing order from shortwave to longwave.
Band 1 Band 2 Band 3 Band 4
Band 5 Band 6 Band 7
Band m
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TemporalTemporal ResolutionResolutionTemporalTemporal ResolutionResolution
June 1, 2006June 1, 2006June 1, 2006June 1, 2006 June 17, 2006June 17, 2006June 17, 2006June 17, 2006 July 3, 2006July 3, 2006July 3, 2006July 3, 2006
Remote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data Acquisition
16 days16 days16 days16 days
Jensen, 2007Jensen, 2007
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Remote Sensing Process
A Energy Source or Illumination B Radiation and the Atmosphere C Interaction with the Target D Recording of Energy by the
Sensor E Transmission, Reception, and
Processing F Interpretation and Analysis G Application
• From Beginning to End
Seven elements of the RS process
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Key Concepts in Remote Sensing
• Digital Image Processing Techniques • a. Preprocessing
Radiometric Correction Geometric Rectification
• b. Image Enhancements • c. Spectral Transformations • d. Atmospheric Corrections • e. Image Classification Techniques
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Pre-processing
Error occurs during data acquisition process
data analysis
it can impact subsequent
Necessary to correct the data
Pre-processing
Sources errors: Internal errors – created by instrument itself External errors – created by platform,
atmosphere, scene characteristics (variable)
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• Aim to corrected image close as possible: radiometrically & geometrically – to radiant energy characteristics of original scene
• Pre-processing operations, sometimes referred to as image restoration and rectification
Pre-processing
Error occurs during data acquisition process
data analysis
it can impact subsequent
Necessary to correct the data
Pre-processing
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Radiometric correction• Radiometric correction is the operation to intend to remove
systematic or random noise affecting the amplitude (brightness) of an image.
• Radiometric problems can be introduced during:– imaging ,
– digitalization,
– transmission.
• Goal to restore an image to the condition it would have been if the imaging process were perfect.
• Example Radiometric problems– striping
– (partially) missing lines
– sensor calibration
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• Exemples
Radiometric Problems
•Line dropout•Striping or banding
noaa15
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Radiometric correction
Radiometric correction is used to modify DN values to account for noise, i.e. contributions to the DN that are a result of…
a. the intervening atmosphere
b. the sun-sensor geometry
c. the sensor itself
We may need to correct for the following reasons:
a. Variations within an image (speckle or striping)
b. between adjacent or overlapping images (for mosaicing)
c. between bands (for some multispectral techniques)
d. between image dates (temporal data) and sensors
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Geometric Distortion
geometric distortion due to:
• the perspective of the sensor optics,
• the motion of the scanning system,
• the motion and (in)stability of the platform,
• the platform altitude and velocity,
• the terrain relief, and
• the curvature and rotation of the Earth.
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Geometric correction
• Account for distortion in image due to motion of platform and scanner mechanism– Particular problem for airborne data: distortion due to
roll, pitch, yaw
From:http://liftoff.msfc.nasa.gov/academy/rocket_sci/shuttle/attitude/pyr.html
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Geometric correction• Airborne data over Barton
Bendish, Norfolk, 1997• Resample using ground control
points– various warping and resampling
methods– nearest neighbour, bilinear or
bicubic interpolation....– Resample to new grid (map)
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Resampling methods
http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf
New DN values are assigned in 3 ways
a.Nearest Neighbour Pixel in new grid gets the value of closest pixel from old grid – retains original DNs b. Bilinear Interpolation New pixel gets a value from the weighted average of 4 (2 x 2) nearest pixels; smoother but ‘synthetic’
c. Cubic Convolution (smoothest)New pixel DNs are computed from weighting 16 (4 x 4) surrounding DNs
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Atmospheric Corrections • Atmospheric mechanisms
AbsorptionScattering
– Rayleigh scattering– Mie scattering– Nonselective scattering
• The aim of atmospheric correction is to retrieve the surface reflectance (that characterizes the surface properties) from remotely sensed imagery by removing the atmospheric effects.
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Atmospheric CorrectionsInteractions with the atmosphere
•Notice that target reflectance is a function of
•Atmospheric irradiance (path radiance: R1)
•Reflectance outside target scattered into path (R2)
•Diffuse atmospheric irradiance (scattered onto target: R3)
•Multiple-scattered surface-atmosphere interactions (R4)
From: http://www.geog.ucl.ac.uk/~mdisney/phd.bak/final_version/final_pdf/chapter2a.pdf
R1
target
R2
target
R3
target
R4
target
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Atmospheric Corrections
Landsat - TM
Band 1, Before Correction Band 1, After Correction
Aim process of removing the effects of the atmosphere on the reflectance values of images taken by satellite or airborne sensors. There are bidirectional and empirical models for doing atmospheric correction on an image.
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Atmospheric correction: simple
• Simple methods– e.g. empirical line correction (ELC) method
– Use target of “known”, low and high reflectance targets in one channel e.g. non-turbid water & desert, or dense dark vegetation & snow
– Assuming linear detector response, radiance, L = gain * DN + offset
– e.g. L = DN(Lmax - Lmin)/255 + Lmin
DN
Radiance, L
Target DN values
Regression line L = G*DN + O (+)
Offset assumed to be atmospheric path radiance (plus dark current signal)
Lmax
Lmin
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Atmospheric correction: complex
• Atmospheric radiative transfer modelling– use detailed scattering models of atmosphere
including gas and aerosols• Second Simulation of Satellite Signal in Solar Spectrum (6s)• MODTRAN/LOWTRAN• SMAC etc.
http://www-loa.univ-lille1.fr/Msixs/msixs_gb.html
http://geoflop.uchicago.edu/forecast/docs/Projects/modtran.doc.html
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Atmospheric correction: complex
• Radiative transfer models such as 6S require:– Geometrical conditions (view/illum. angles)– Atmospheric model for gaseous components (Rayleigh
scattering)• H2O, O3, aerosol optical depth, (opacity)
– Aerosol model (type and concentration) (Mie scattering)• Dust, soot, salt etc.
– Spectral condition• bands and bandwidths
– Ground reflectance (type and spectral variation)• surface BRDF (default is to assume Lambertian….)
• If no info. use default values (Standard Atmosphere)
From: http://www.geog.ucl.ac.uk/~mdisney/phd.bak/final_version/final_pdf/chapter2a.pdf
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Atmospheric Correction Using Atmospheric Correction Using ATCORATCOR
Atmospheric Correction Using Atmospheric Correction Using ATCORATCOR
a) Image containing substantial haze prior to atmospheric correction. b) a) Image containing substantial haze prior to atmospheric correction. b) Image after atmospheric correction using ATCOR (Courtesy Leica Image after atmospheric correction using ATCOR (Courtesy Leica Geosystems and DLR, the German Aerospace Centre). Geosystems and DLR, the German Aerospace Centre).
a) Image containing substantial haze prior to atmospheric correction. b) a) Image containing substantial haze prior to atmospheric correction. b) Image after atmospheric correction using ATCOR (Courtesy Leica Image after atmospheric correction using ATCOR (Courtesy Leica Geosystems and DLR, the German Aerospace Centre). Geosystems and DLR, the German Aerospace Centre).
Jensen 2005
Jensen 2005
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Image Enhancement
• The objective of image enhancement is to process an image so that the result is more suitable than the original image for a specific application.
• There are two main approaches:– Image enhancement in spatial domain: Direct
manipulation of pixels in an image• Point processing: Change pixel intensities• Spatial filtering
– Image enhancement in frequency domain: Modifying the Fourier transform of an image
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Image Enhancement
• Enhancement means alteration of the appearance of an image in such a way that the information contained in that image is more readily interpreted visually in terms of a particular need.
• The image enhancement techniques are applied either to single-band images or separately to the individual bands of a multi-band image set.
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Image Enhancement by Point Processing
• Histogram Equalization
Histogram of an image represents the relative frequency of occurrence of various gray levels in the image
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Spatial Filtering• Spatial filtering - encompasses another set of digital
processing functions which are used to enhance the appearance of an image.
Spatial filter is based on central pixel and its neighbors pixels.
The dimension of filter is odd number (3x 3, 5 x 5, 7x7…)
3x35X57X7
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Spatial FilteringThe filtering procedure involves moving a 'window' of a few pixels in dimension over each pixel in the image, applying a mathematical calculation using the pixel values under that window, and replacing the central pixel with the new value. The window is moved along in both the row and column dimensions one pixel at a time and the calculation is repeated until the entire image has been filtered and a "new" image has been generated.
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(8+6+6+2+7+6+2+2+6)/9 = 5,
Mean
5
Median
[ 2 2 2 6 6 6 6 7 8] = 6 , 6
Simple Example of Spatial Filtering
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Spatial Filtering
Original
3x3 averaging
filter
Salt&Pepper noise added
3x3 median filter
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Image TransformationImage transformations typically involve the manipulation of multiple bands of data, or from two or more images of the same area acquired at different times (i.e. multi-temporal image data).
Image transformations generate "new" images from two or more sources which highlight particular features or properties of interest, better than the original input images.
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Image Classification and Analyses
Supervised classification, the analystidentifies in the imagery homogeneous representative samples of the different surface cover types (information classes) of interest.
These samples ( training areas), which is based on the analyst's familiarity with the geographical area. Thus, the analyst is "supervising" thecategorization of a set of specific classes.
This used to "train" the computer to recognize spectrally similar areas for each class.
Each pixel in the image is compared to these signatures and labeled as the class it most closely "resembles" digitally.
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Image Classification and AnalysesUnsupervised classification in essence reverses the supervised classification process.
Spectral classes are grouped first, based solely on the numerical information in the data, and are then matched by the analyst to information classes (if possible).
Programs, called clustering algorithms, are used to determine the natural (statistical) groupings or structures in the data. Usually, the analyst specifies how many groups or clusters are to be looked for in the data. In addition to specifying the desired number of classes, the analyst may also specify parameters related to the separation distance among the clusters and the variation within each cluster.
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Image Classification and Analyses
The image analyses for particular study can be better performed combining different sources of information associated to the study location (ex. maps, images from different time, image with different spatial resolution and platforms, etc.) and tool (Geographical information system ). And the data inferred and the remote sensing process can be evaluate by comparison with ground truth
It is interesting to perform analyses usingmultitemporal,multiresolution,multisensor,multi-data type in nature.
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• http://www.gis.unbc.ca/courses/geog432/lectures/lect7/index.php
• http://www.eurimage.com/products/landsat.html
• http://www.rrcap.unep.org/lc/cd/html/training/module2s1.html
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Type Pass Filter
High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image.
A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image.
1 1 1 1 2 11 1 1 2 4 21 1 1 1 2 1
0 -1 0 -1 5 -10 -1 0 -1 -1 -1-1 9 -1 -1 -1 -1