1. Remote Sensing Data Collection
The amount of electromagnetic radiance, L (watts m-2 sr-1; watts per meter squared 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:
where,
= wavelength (spectral response measured in various bands or at specific frequencies). sx,y,z = x, y, z location of the picture element and its size (x, y), t = temporal information, i.e., when and how often the information was acquired, q = 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 field), and the remote sensing system. P = polarization of back-scattered energy recorded by the sensor, W = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.

2. Remote Sensing Data Collection
sx,y,z = x, y, z location of the picture element and its size (x, y)
t = temporal information, i.e., when and how often the information was acquired
q = 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 field), and the remote sensing system
P = polarization of back-scattered energy recorded by the sensor
W = radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.

3. Platforms Geostationary Altitudes aprox. 36,000 kilometers
Landsat satellites have near polar orbits which are sun-synchronous. That is, they have near circular orbits which are nearly north-south in their heading. They are sun-synchronous because they pass over the same latitude at the same local time every day. This ensures consistent illumination conditions when acquiring images in a specific season over successive years, or over a particular area over a series of days.
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.

4. Platforms Polar Orbit Altitudes aprox. 800 kilometres
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.
Landsat satellites have near polar orbits which are sun-synchronous. That is, they have near circular orbits which are nearly north-south in their heading. They are sun-synchronous because they pass over the same latitude at the same local time every day. This ensures consistent illumination conditions when acquiring images in a specific season over successive years, or over a particular area over a series of days.
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.

5. 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.

6. 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

7. DIGITAL IMAGE
A photograph could also be represented and displayed in a
digital format by subdividing the image into small equal-sized and
shaped areas, called picture elements or pixels, and representing the brightness of each area with a numeric value or digital number.

8. Digital Image
Digital Number
128
255

9. Remote Sensor Resolution
• Spatial the size of the field-of-view, e.g. 10 x 10 m.
• Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared
thermal infrared, microwave (radar).
• Temporal - how often the sensor acquires data, e.g. every 30 days.
• Radiometric - the sensitivity of detectors to small differences in electromagnetic energy.
10 m B G R
NIR
Jan 15
Feb 15
Jensen, 2007

10. Radiometric Resolution
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)
O número de níveis de cinza é comumente expresso em função do número de dígitos
binários (bits) necessários para armazenar, em forma digital, o valor do nível máximo.
O valor em bits é sempre uma potência de 2. Assim, 5 bits significam (2)5= 32 níveis de cinza.
Os satélites LANDSAT e SPOT têm resolução radiométrica de 8 bits, o que significa o registro de imagens em 256 níveis de cinza.
The radiometric resolution of an imaging system describes its ability to discriminate very slight differences in energy. .

11. Radiometric Resolution
By comparing a 2-bit image with an 8-bit image, we can see that there is
a large difference in the level of detail discernible depending on their radiometric resolutions.
Resolução = 2 bits = 22 = 4 níveis de cinza
Resolução = 8 bits = 28 = 256 níveis de cinza

12. Radiometric Resolution
7-bit
( )
8-bit
( )
9-bit
( )
10-bit
( )
Jensen, 2007

13. 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.

14. Spatial Resolution
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.
Jensen, 2007

15. Spatial resolution
Sensors

16. 16

17. 17

18. 18

19. 19

20. Spectral Resolution
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.

21. Bands
Satellite sensors measure energy from particular set of wavelengths dl, which are referred to as “bands” and numbered in increasing order from shortwave to longwave.
Band dl (mm)
Band 1
Band 2
Band 3
Band 4
Band 5
Band 6
Band 7

22. Remote Sensor Data Acquisition
Temporal Resolution
Remote Sensor Data Acquisition
June 1, 2006
June 17, 2006
July 3, 2006
16 days
Jensen, 2007

23. Remote Sensing Process
From Beginning to End
Seven elements of the RS 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
The remote sensing data-collection and analysis procedures used for Earth resource applications are often implemented in a systematic fashion referred to as the remote sensing process.

24. 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
As imagens brutas, ou seja, sem nenhum tipo de correção, contém algumas distorções radiométricas e geométricas que devem ser corrigidas antes de serem usadas nas
aplicações. Esta etapa de correção das distorções é conhecida como pré-processamento. A utilização de técnicas de pré-processamento constitui uma das etapas mais importantes do processamento digital. Do mesmo modo que as técnicas de realce enfatizam feições de interesse na imagem elas também realçam as imperfeições na mesma. Sendo assim, é conveniente que ruídos ou outras imperfeições intrínsecas nas cenas sejam removidas ou atenuadas antes da aplicação de técnicas de realce. Muitas vezes, as imperfeições são inerentes e dependentes do sistema sensor utilizado para gerar a imagem digital. Isto faz com que algoritmos específicos para remoção ou redução de ruídos sejam desenvolvidos, dependendo do tipo de ruído presente. Embora alguns tipos de distorções sejam corrigidas na estação de recepção de imagens há ainda necessidade de se realizar algumas correções antes da fase de processamento de imagens, propriamente dita.

25. Error occurs during data acquisition process
Pre-processing
it can impact subsequent
Error occurs during data acquisition process
data analysis
Necessary to correct the data
Pre-processing
refers to the initial processing of raw image to correct the geometric distortions, calibrate the data radiometrically and eliminate the noise and clouds that present in the data. These operations are called preprocessing because they normally carried out before the real analysis and manipulations of the data occur in order to extract any specific information. The aim is to correct the distorted or degraded image data to create a more faithful representation of the real scene.
Sources errors:
Internal errors – created by instrument itself
External errors – created by platform,
atmosphere,
scene characteristics (variable)

26. Error occurs during data acquisition process
Pre-processing
it can impact subsequent
Error occurs during data acquisition process
data analysis
Necessary to correct the data
Pre-processing
refers to the initial processing of raw image to correct the geometric distortions, calibrate the data radiometrically and eliminate the noise and clouds that present in the data. These operations are called preprocessing because they normally carried out before the real analysis and manipulations of the data occur in order to extract any specific information. The aim is to correct the distorted or degraded image data to create a more faithful representation of the real scene.
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

27. 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

28. Radiometric Problems Exemples noaa15 Line dropout Striping or banding
Common forms of noise include systematic striping or banding and dropped lines. Both of these effects should be corrected before further enhancement or
classification is performed.
Dropped lines occur when there are systems errors which result in missing or defective data along a scan line. Dropped lines are normally 'corrected‘ by replacing the line with the pixel values in the line above or below, or with the average of the two.
Line dropout
Striping or banding

29. 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

30. 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.
Any remote sensing image, regardless of whether it is acquired by a multispectral scanner on board a satellite, a photographic system in an aircraft, or any other platform/sensor combination, will have various geometric distortions. This problem is inherent in remote sensing, as we attempt to accurately represent the three-dimensional surface of the Earth as a two-dimensional image.

31. 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:

32. 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)

33. Resampling methods 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

34. Atmospheric Corrections
Atmospheric mechanisms
Absorption
Scattering
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.
However, the large amounts of imagery collected by the satellites are largely contaminated by the effects of atmospheric particles through absorption and scattering of the radiation from the earth surface. The objective of atmospheric correction is to retrieve the surface reflectance (that characterizes the surface properties) from remotely sensed imagery by removing the atmospheric effects. Atmospheric correction has been shown to significantly improve the accuracy of image classification.

35. Atmospheric Corrections
Interactions with the atmosphere R 1
target R 3
target R 4
target R 2
target
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:

36. Atmospheric Corrections
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.
Landsat - TM
Band 1, Before Correction Band 1, After Correction

37. 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
Offset assumed to be atmospheric path radiance (plus dark current signal)
Lmax
Regression line L = G*DN + O (+)
Target DN values
Lmin

38. 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.

39. 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:

40. Atmospheric Correction Using ATCOR
a) Image containing substantial haze prior to atmospheric correction. b) Image after atmospheric correction using ATCOR (Courtesy Leica Geosystems and DLR, the German Aerospace Centre).
Jensen 2005

41. 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

42. 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.

43. Image Enhancement by Point Processing
Histogram Equalization
Histogram of an image represents the relative frequency of occurrence of various gray levels in the image

44. 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.
3x3
5X5
7X7
The dimension of filter is odd number (3x 3, 5 x 5, 7x7…)

45. Spatial Filtering
The 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.

46. Simple Example of Spatial Filtering
Mean
( )/9 = 5, 5
Median
Order from the Lower brightness number to higher
[ ] = 6 , 6

47. Spatial Filtering Salt&Pepper noise added Original
3x3 averaging filter
3x3 median filter

48. Image Transformation
Image 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.

49. Image Classification and Analyses
Supervised classification, the analyst
identifies 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" the
categorization 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.
and their knowledge of the actual surface cover types present in the image

50. Image Classification and Analyses
Unsupervised 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.

51. 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 using
multitemporal,
multiresolution,
multisensor,
multi-data type in nature.

57. Type Pass Filter
A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image.
High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image.
Thus, low-pass filters generally serve to smooth the appearance of an image. Average and median filters, often used for radar imagery, are examples of low-pass filters.
One implementation of a high-pass filter first applies a low-pass filter to an image and then subtracts the result from the original, leaving behind only the high spatial frequency information.