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

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

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

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.

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

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

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.

Digital Image

0 128 255

Digital Number

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

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. .

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.

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

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.

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.

Spatial resolutionSpatial resolution

• Sensors

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

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

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

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

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

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)

• 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

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

• Exemples

Radiometric Problems

•Line dropout•Striping or banding

noaa15

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

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.

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

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)

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

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.

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

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.

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

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

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

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

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

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.

Image Enhancement by Point Processing

• Histogram Equalization

Histogram of an image represents the relative frequency of occurrence of various gray levels in the image

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

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.

(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

Spatial Filtering

Original

3x3 averaging

filter

Salt&Pepper noise added

3x3 median filter

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.

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.

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.

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.

• 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

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