Radiometric Correction. Lecture 4 February 5, 2008

Transcription

1 Radiometric Correction Lecture 4 February 5, 2008

2 Procedures of image processing Preprocessing Radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. Detector error correction, Atmospheric and topographic corrections Geometric correction is concerned with placing the above measurements or derivative products in their proper locations. Information enhancement Point operations change the value of each individual pixel independent of all other pixels Local operations change the value of individual pixels in the context of the values of neighboring pixels. Information enhancement includes image reduction, image magnification, transect extraction, contrast adjustments (linear and non-linear), band ratioing, spatial filtering, fourier transformations, principle components analysis, and texture transformations Information extraction Post-classification Information output Image or enhanced image itself, thematic map, vector map, spatail database, summary statistics and graphs

3 Why corrections The perfect remote sensing system has yet to be developed. The Earth s atmosphere, land, and water are amazingly complex and do not lend themselves well to being recorded by remote sensing devices. Error sources: Internal errors are introduced by the remote sensing system. They are generally systematic (predictable) and may be identified and then corrected based on prelaunch or in-flight calibration measurements. For example, n-line striping in the imagery may be caused by a single detector that has become uncalibrated. In many instances, radiometric correction can adjust for detector miscalibration. External errors are introduced by phenomena that vary in nature through space and time. External variables that can cause remote sensor data to exhibit radiometric and geometric error include the atmosphere, terrain elevation, slope, and aspect. Some external errors may be corrected by relating empirical ground observations (i.e., radiometric and geometric ground control points) to sensor measurements.

4 Types of radiometric correction Detector error or sensor error (internal error) Atmospheric error (external error) Topographic error (external error)

5 1. Correcting detector or sensor error Ideally, the radiance recorded by a remote sensing system in various bands is an accurate representation of the radiance actually leaving the feature of interest (e.g., soil, vegetation, atmosphere, water, or urban land cover) on the Earth s surface or atmosphere. Unfortunately, noise (error) can enter the data-collection system at several points. For example, radiometric error in remotely sensed data may be introduced by the sensor system itself when the individual detectors do not function properly or are improperly calibrated. Several of the more common remote sensing system induced radiometric errors are: random bad pixels (shot noise), line-start/stop problems, line or column drop-outs, partial line or column drop-outs, and line or column striping.

6 1.1 Random bad pixels (shot noise) Sometimes an an individual detector does does not not record spectral data data for for an an individual pixel. When this this occurs randomly, it it is is called a bad bad pixel. When there there are are numerous random bad bad pixels found within the the scene, it it is is called shot shot noise because it it appears that that the the image was was shot shot by by a shotgun. Normally these these bad bad pixels contain values of of 0 or or (in (in 8-bit 8-bit data) data) in in one one or or more more of of the the bands. Shot Shot noise is is identified and and repaired using the the following methodology. It It is is first first necessary to to locate each each bad bad pixel pixel in in the the band band k dataset. A simple thresholding algorithm makes a pass pass through the the dataset and and flags flags any any pixel pixel (BV (BV i,j,k ) i,j,k ) having a brightness value of of zero zero (assuming values of of 0 represent shot shot noise and and not not a real real land land cover such such as as water). Once identified, it it is is then then possible to to evaluate the the eight eight pixels surrounding the the flagged pixel, as as shown below:

7 a) a) Landsat Thematic Mapper band band 7 (2.08 ( µm) µm) image of of the the Santee Delta in in South Carolina. One One of of the the detectors exhibits serious striping and and the the absence of of brightness values at at pixel pixel locations along a scan scan line. line. b) b) An An enlarged view view of of the the bad bad pixels with with the the brightness values of of the the eight eight surrounding pixels annotated. c) c) The The brightness values of of the the bad bad pixels after after shot shot noise removal. This This image was was not not destriped. BV i, j, k = int 8 i= 1 BV 8 i

8 1.2 line-start/stop problems Occasionally, scanning systems fail to collect data at the beginning or end of a scan line, or they place the pixel data at inappropriate locations along the scan line. For example, all of the pixels in a scan line might be systematically shifted just one pixel to the right. This is called a line-start problem. Also, a detector may abruptly stop collecting data somewhere along a scan and produce results similar to the line or column drop-out previously discussed. Ideally, when data are not collected, the sensor system would be programmed to remember what was not collected and place any good data in their proper geometric locations along the scan. Unfortunately, this is not always the case. For example, the first pixel (column 1) in band k on line i (i.e., BV 1,i,k ) might be improperly located at column 50 (i.e., BV 50,i,k ). If the line-start problem is always associated with a horizontal bias of 50 columns, it can be corrected using a simple horizontal adjustment. However, if the amount of the line-start displacement is random, it is difficult to restore the data without extensive human interaction on a line-by-line basis. A considerable amount of MSS data collected by Landsats 2 and 3 exhibit line-start problems.

9 Infrared imagery of the Four Mile Creek thermal effluent plume entering the Savannah River

10 1.3 line or column drop-outs An entire line containing no spectral information may be produced if an individual detector in a scanning system (e.g., Landsat MSS or Landsat 7 ETM+) fails to function properly. If a detector in a linear array (e.g., SPOT XS, IRS-1C, QuickBird) fails to function, this can result in an entire column of data with no spectral information. The bad line or column is commonly called a line or column drop-out and contains brightness values equal to zero. For example, if one of the 16 detectors in the Landsat Thematic Mapper sensor system fails to function during scanning, this can result in a brightness value of zero for every pixel, j, in a particular line, i. This line drop-out would appear as a completely black line in the band, k, of imagery. This is a serious condition because there is no way to restore data that were never acquired. However, it is possible to improve the visual interpretability of the data by introducing estimated brightness values for each bad scan line. It is first necessary to locate each bad line in the dataset. A simple thresholding algorithm makes a pass through the dataset and flags any scan line having a mean brightness value at or near zero. Once identified, it is then possible to evaluate the output for a pixel in the preceding line (BV i 1, j, k ) and succeeding line (BV i+1, j, k ) and assign the output pixel (BV i, j, k ) in the drop-out line the average of these two brightness values BV i, j, k BVi 1, j, k + = int 2 BV i+ 1, j, k

11 1.4 partial line or column drop-outs Only portion of a line or column drop-outs

12 1.5 line or column striping Sometimes a detector does not fail completely, but simply goes out of radiometric adjustment. For example, a detector might record spectral measurements over a dark, deep body of water that are almost uniformly 20 brightness values greater than the other detectors for the same band. The result would be an image with systematic, noticeable lines that are brighter than adjacent lines. This is referred to as n-line striping. The maladjusted line contains valuable information, but should be corrected to have approximately the same radiometric scale as the data collected by the properly calibrated detectors associated with the same band. To repair systematic n-line striping, it is first necessary to identify the miscalibrated scan lines in the scene. This is usually accomplished by computing a histogram of the values for each of the n detectors that collected data over the entire scene (ideally, this would take place over a homogeneous area, such as a body of water). If one detector s mean or median is significantly different from the others, it is probable that this detector is out of adjustment. Consequently, every line and pixel in the scene recorded by the maladjusted detector may require a bias (additive or subtractive) correction or a more severe gain (multiplicative) correction. This type of n-line striping correction a) adjusts all the bad scan lines so that they have approximately the same radiometric scale as the correctly collected data and b) improves the visual interpretability of the data. It looks better. To repair non-systematic striping, there is no easy way.

13 Systematic stripping

14 Non-systematic Striping CPCA Combined Principle Component Analysis Xie et al. 2004

15 2. Atmospheric correction Various Paths of Satellite Received Radiance There are several ways to atmospherically correct remotely sensed data. Some are relatively straightforward while others are complex, being founded on physical principles and requiring a significant amount of information to function properly. This discussion will focus on two major types of atmospheric correction: Absolute atmospheric correction, and Relative atmospheric correction. Solar irradiance Diffuse sky irradiance E d E 0 T θ Þ 4 θ 0 Total radiance at the sensor θ v L I 1,3,5 L p L T T θ v L S Remote sensor detector 60 miles or 100km Atmosphere 5 Scattering, Absorption Refraction, Reflection Reflectance from neighboring area, r λ n Reflectance from study area, r λ

16 Path Path 1 contains spectral solar solar irradiance ( E ( oλ )) that that was was attenuated very very little little before illuminating the the terrain within the the IFOV. Notice in in this this case case that that we we are are interested in in the the solar solar irradiance from from a specific solar solar zenith angle ( θ ( o )) and and that that the the amount of of irradiance reaching the the terrain is is a function of of the the atmospheric transmittance at at this this angle ( T ( θo ). ). If If all all of of the the irradiance makes it it to to the the ground, then then the the atmospheric transmittance ( T ( θo )) equals one. one. If If none none of of the the irradiance makes it it to to the the ground, then then the the atmospheric transmittance is is zero zero

17 Path Path 2 contains contains spectral spectral diffuse diffuse sky sky irradiance ( E ( dλ )) that that never never even even reaches reaches the the Earth s Earth s surface surface (the (the target target study study area) area) because because of of scattering in in the the atmosphere. Unfortunately, such such energy energy is is often often scattered directly directly into into the the IFOV IFOV of of the the sensor sensor system. system. As As previously discussed, Rayleigh scattering of of blue blue light light contributes much much to to this this diffuse diffuse sky sky irradiance. That That is is why why the the blue blue band band image image produced by by a a remote remote sensor sensor system system is is often often much much brighter brighter than than any any of of the the other other bands. bands. It It contains contains much much unwanted diffuse diffuse sky sky irradiance that that was was inadvertently scattered into into the the IFOV IFOV of of the the sensor sensor system. system. Therefore, if if possible, we we want want to to minimize its its effects. effects. Green Green (2003) (2003) refers refers to to the the quantity quantity as as the the upward upward reflectance of of the the atmosphere (( E duλ ). ).

18 Path Path 3 contains energy from from the the Sun Sun that that has has undergone some Rayleigh, Mie, Mie, and/or nonselective scattering and and perhaps some absorption and and reemission before illuminating the the study area. area. Thus, its its spectral composition and and polarization may may be be somewhat different from from the the energy that that reaches the the ground from from path path Green (2003) refers to to this this quantity as as the the downward reflectance of of the the atmosphere ( E ( ddλ ). ).

19 Path Path 4 contains radiation that that was was reflected or or scattered by by nearby terrain ( ρ ( λn )) covered by by snow, concrete, soil, soil, water, and/or vegetation into into the the IFOV of of the the sensor system. The The energy does does not not actually illuminate the the study area area of of interest. Therefore, if if possible, we we would like like to to minimize its its effects. Path Path 2 and and Path Path 4 combine to to produce what what is is commonly referred to to as as Path Path Radiance, L p. p.

20 Path Path 5 is is energy that that was was also also reflected from from nearby terrain into into the the atmosphere, but but then then scattered or or reflected onto onto the the study area. area.

21 The The total total radiance reaching the the sensor is: is: 1 L S = ρ Tθ + v λ o π ( Eo λtθ cosθ o + Ed ) Lp This This may may be be summarized as: as: L = L + L S T p

22 2.1 Absolute atmospheric correction Solar radiation is largely unaffected as it travels through the vacuum of space. When it interacts with the Earth s atmosphere, however, it is selectively scattered and absorbed. The sum of these two forms of energy loss is called atmospheric attenuation. Atmospheric attenuation may 1) make it difficult to relate hand-held in situ spectroradiometer measurements with remote measurements, 2) make it difficult to extend spectral signatures through space and time, and (3) have an impact on classification accuracy within a scene if atmospheric attenuation varies significantly throughout the image. The general goal of absolute radiometric correction is to turn the digital brightness values (or DN) recorded by a remote sensing system into scaled surface reflectance values. These values can then be compared or used in conjunction with scaled surface reflectance values obtained anywhere else on the planet.

23 2.1.1 Radiative transfer-based atmospheric correction algorithms Much research has been carried out to address the problem of correcting images for atmospheric effects. These efforts have resulted in a number of atmospheric radiative transfer codes (models) that can provide realistic estimates of the effects of atmospheric scattering and absorption on satellite imagery. Once these effects have been identified for a specific date of imagery, each band and/or pixel in the scene can be adjusted to remove the effects of scattering and/or absorption. The image is then considered to be atmospherically corrected. Unfortunately, the application of these codes to a specific scene and date also requires knowledge of both the sensor spectral profile and the atmospheric properties at the same time. Atmospheric properties are difficult to acquire even when planned. For most historic satellite data, they are not available. Even today, accurate scaled surface reflectance retrieval is not operational for the majority of satellite image sources used for land-cover change detection. An exception is NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), for which surface reflectance products are available.

24 Cont Most current radiative transfer-based atmospheric correction algorithms can compute much of the required information if a) the user provides fundamental atmospheric characteristic information to the program or b) certain atmospheric absorption bands are present in the remote sensing dataset. For example, most radiative transfer-based atmospheric correction algorithms require that the user provide: latitude and longitude of the remotely sensed image scene, date and exact time of remote sensing data collection, image acquisition altitude (e.g., 20 km AGL) mean elevation of the scene (e.g., 200 m ASL), an atmospheric model (e.g., mid-latitude summer, mid-latitude winter, tropical), radiometrically calibrated image radiance data (i.e., data must be in the form W m2 mm-1 sr-1), data about each specific band (i.e., its mean and full-width at half-maximum (FWHM), and local atmospheric visibility at the time of remote sensing data collection (e.g., 10 km, obtained from a nearby airport if possible).

25 Cont These parameters are then input to the atmospheric model selected (e.g., mid-latitude summer) and used to compute the absorption and scattering characteristics of the atmosphere at the instance of remote sensing data collection. These atmospheric characteristics are then used to invert the remote sensing radiance to scaled surface reflectance. Many of these atmospheric correction programs derive the scattering and absorption information they require from robust atmosphere radiative transfer code such as MODTRAN 4+ or Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Examples include: ACORN ATCOR ATREM FLAASH (we have)

26 a) a) Image Image containing substantial haze haze prior prior to to atmospheric correction. b) b) Image Image after after atmospheric correction using using ATCOR (Courtesy Leica Leica Geosystems and and DLR, DLR, the the German German Aerospace Centre). Centre).

27 2.1.2 Empirical Line Calibration Absolute atmospheric correction may may also also be be performed using empirical line line calibration (ELC), which forces the the remote sensing image data data to to match in in situ situ spectral reflectance measurements, hopefully obtained at at approximately the the same same time time and and on on the the same same date date as as the the remote sensing overflight. Empirical line line calibration is is based on on the the equation: Reflectance (field spectrum) = gain x radiance (image) + offset

28 a) a) Field Field crew crew taking taking a a spectroradiometer measurement (total (total incoming radiance) from from a a calibrated white white board board on on the the tripod. tripod. b) b) 8 8 m black black and and white white calibration targets targets at at the the Savannah River River Site Site to to be be measured as as reflected or or outgoing radiance ρ λ = outgoing _ radiance total _ inco min g _ radiance reflectivity from in situ measurements y = x R 2 = radiance from image

29 If the in situ was not possible, you can use the Spectral library or measurements after. The point is to find several homogeneous targets (white and black) The example here only used one pixel of water and one pixel of beach Result indicates correct chlorophyll absorption in the blue (band 1) and red (band 3) portions of the spectrum and the increase in near-infrared reflectance

30 2.2 relative radiometric correction When required data is not available for absolute radiometric correction, we can do relative radiometric correction Relative radiometric correction may be used to Single-image normalization using histogram adjustment Multiple-data image normalization using regression

31 2.2.1 Single-image normalization using histogram adjustment The method is based on the fact that infrared data (>0.7 µm) is free of atmospheric scattering effects, whereas the visible region ( µm) is strongly influenced by them. Use Dark Subtract to apply atmospheric scattering corrections to the image data. The digital number to subtract from each band can be either the band minimum, an average based upon a user defined region of interest, or a specific value

32 Dark Subtract using band minimum

33 2.2.2 Multiple-date image normalization using regression Selecting a base image and then transforming the spectral characteristics of all other images obtained on different dates to have approximately the same radiometric scale as the based image. Selecting a pseudo-invariant features (PIFs) or region (points) of interest is important: Spectral characteristic of PIFs change very little through time, (deep water body, bare soil, rooftop) PIFs should be in the same elevation as others No or rare vegetation, The PIF must be relatively flat Then PIFs will be used to normalize the multiple-date imagery

34 Example SPOT Band 3, 8/10/91 SPOT Band 1, 8/10/91 SPOT image of 8/10/1991 is selected as the base image PIFs (wet and dry) were selected for generating the relationship between the base image and others The resulted regression equation will be used to normalize the entire image of 4/4/87 to 8/10/91 for change detection. The additive component corrects the path radiance among dates, and multiplicative term correct the detector calibration, sun angle, earth-sun distance, atmospheric attenuation, and phase angle between dates.

35 Regression equations for all images, all based on the SPOT image of 8/10/91

36 2.2.3 other relative radiometric correction methods (ENVI) Use Flat Field calibration to normalize images to an area of known "flat" reflectance. This is particularly effective for reducing hyperspectral data to relative reflectance. The method requires that you select a Region Of Interest (ROI) prior to execution. The average spectrum from the ROI is used as the reference spectrum, which is then divided into the spectrum at each pixel of the image Use IAR Reflectance calibration (Internal Average Relative Reflectance) to normalize images to a scene average spectrum. This is particularly effective for reducing hyperspectral data to relative reflectance in an area where no ground measurements exist and little is known about the scene. It works best for arid areas with no vegetation. An average spectrum is calculated from the entire scene and is used as the reference spectrum, which is then divided into the spectrum at each pixel of the image

37 3. Topographic correction Topographic slope and aspect also introduce radiometric distortion (for example, areas in shadow) The goal of a slope-aspect correction is to remove topographically induced illumination variation so that two objects having the same reflectance properties show the same brightness value (or DN) in the image despite their different orientation to the Sun s position Based on DEM, sun-elevation

38 Image acquisition geometry Sun zenith angle is the angle of Sun away from vertical Sun elevation angle is the angle of Sun away from horizontal Sensor elevation angle is the angle away from horizontal Sensor azimuth angle and Sun azimuth are clockwise from the north Source: IKONOS geometry at

39 1. Cosine correction L H = L T cosθ 0 cosi 2. Minnaert correction cosθ0 L H = LT ( ) cosi 3. statistical-empirical correction k L = L mcosi b + H T L T 4. C correction L H = L T cosθ 0 + c cosi + c

40 computing the cosine of the solar incidence angle cos(i) = sin( δ )sin( φ)cos( s) sin( δ )cos( φ)sin( s)cos( γ ) + cos( δ )cos( φ)cos( s)cos( ω) + cos( δ )sin( φ)sin( s)cos( γ )cos( ω) + cos( δ )sin( φ)sin( s)sin( ω) where, δ declination of the earth (positive in summer in northern hemisphere) φ latitude of the pixel (positive for northern hemisphere) s slope in radians, where s=0 is horizontal and s=π/2 is vertical downward (s is always positive and represents a downward slope in any direction) γ surface azimuth angle. γ is the deviation of the normal to the surface from the local meridian, where γ = 0 for aspect that is due south, γ = - for east and γ = + for western aspect. γ = -π/2 represents an east-facing slope and γ = +π/2 represents an west-facing slope. γ = -π or γ = π represents a north-facing slope. ω hour angle. ω = 0 at solar noon, ω is negative in morning and ω is positive in afternoon Source: Duffie,J.A. and W.A.Beckman, Solar engineering of thermal processes. John Wiley and Sons, NY.

41 If you are interested in, please read this source paper from: Law and Nichol