Radiometric Correction Objectives • To understand Empirical Line Calibration. • To perform ELC using the ENVI system. Part I - Empirical Line Calibration ________________________________________ The following description of Empirical Line Calibration (ELC) is directly from the text by Dr. Jensen (2015). If you want more specific knowledge of ELC, please refer to Jensen (2015): pp 220-223. Atmospheric correction can be performed using Empirical Line Calibration (ELC). ELC has made the remote sensing data match in situ spectral reflectance measurements, which are obtained at approximately the same time as the remote sensing platform overflight. ELC is based on the equation: BVk = Ak * k + Bk, where, BVk is the digital brightness value for a pixel of band k; k equals the in situ surface reflectance of the materials within the remote sensor IFOV at a specific wavelength; Ak is a multiplicative term (gain) affecting the BVk, and Bk is an additive term (offset). The multiplicative term is associated primarily with atmospheric transmittance and instrumental factors, and the additive term deals primarily with atmospheric path radiance and instrumental offset (i.e., dark current). To use ELC, the analyst usually selects two or more areas in the scene with different albedos (e.g., one bright target such as a sand pile and one dark target such as a deep, nonturbid water body). The areas should be as homogeneous as possible. In situ spectroradiometer measurements of these targets are made on the ground. The in situ and remote sensing–derived spectra are regressed and gain and offset values computed. The gain and offset values are then applied to the remote sensor data on a band by band basis, removing atmospheric attenuation. Note that the correction is applied band by band and not pixel by pixel. Most multispectral remote sensing datasets can be calibrated using the empirical line calibration. The difficulty arises when trying to locate homogeneous bright and dark targets in the study, collecting representative in situ spectroradiometer measurements, and extracting uncontaminated pixels of the calibration targets from the imagery. If the analyst does not have access to in situ spectra obtained at the time of the remote sensing overflight, it might be possible to use spectra of such fundamental materials as clear water and sand (quartz) that are stored in spectral libraries (e.g., from NASA’s Jet Propulsion Laboratory (JPL), USGS, Johns Hopkins University spectral library). Hopefully, some of these materials exist in the scene and the analyst can locate the appropriate pixel and pair the image brightness values with the library in situ spectroradiometer data. You will use several spectral pairs to perform the Empirical Line Calibration. Those pairs are used to plot a regression line, which is used to modify the input image. Since the purpose of 1 the ELC is to define the regression line, you should use at least one pair of spectra from both bright and dark areas. Part II - Perform Empirical Line Calibration Using the ENVI ELC Tools ________________________________________ WorldView-2 Imagery File name: msr4c1p001.img Location: Tampa Bay, FL, USA Sensor: WorldView-2 Bands: Coastal Blue (425 nm), Blue (480 nm), Green (545 nm), Yellow (605 nm), Red (660 nm), Red-Edge (725 nm), NIR1 (833 nm), NIR2 (950 nm). Spatial resolution: 2 m × 2 m Acquisition date: May 1, 2011 In situ spectral measurements In this exercise, we will use in situ spectral measurements taken from beach and bay waters as ground based spectra. The in situ spectra, including two pints from beach (sand1.txt and sand2.txt) and three points from bay/seawaters (water1.txt, water2.txt, and water3.txt), were measured on May 3, 2011. See the in situ spectral measurements in Lab#2 folder. Open msr4c1p001.img for Empirical Line Calibration Now you are ready to use or open the msr4c1p001.img by clicking pull down menu File to select Open (or directly click Open icon, then navigate to your flash drive (or to where you saved the image file) to click image file msr4c1p001.img. Then you can find the image bands listed in Data Manager panel. From the panel, Select Band7 (NIR1), Band 5 (Red) and Band3 (Green) for RGB color guns to display a (standard) false color composite image. Save Dark and Bright Image/pixel Spectra (spectral profiles) in Spectral Library For the exercises, we need two white (from sand beach) and three dark (from seawaters) image/pixel spectra (BV) and corresponding sands/waters in situ spectral measurements to simulate regression lines for ELC. To do so, first locate and export the five image/pixel spectra (two corresponding in situ sand spectra and three corresponding in situ seawater spectra) in spectral library. So, from the Image Window Viewer to press Display/Profiles/Spectral (or click its icon), then type in sample 325, line 2268 in to locate the 1st sand/beach pixel to show up a Spectral Profile for the pixel (spectrum, Figure 1a). You need to export/save the sand (water) image/pixel spectrum (spectral profile in .sli) by pressing Export/Spectral Library… from Spectral Profile panel (click OK to save the 1st sand/beach pixel spectral profile in the folder with the lab data) (Figure 2). Repeatedly, you can locate/export spectral profiles for the 2nd sand and three water pixels one by one by using the same steps to locate/export the 1st sane pixel spectral profile. The sample # and line # for the 2nd sand/beach and three water pixels are sample 306, line 2325 for the 2nd sane/beach pixel; sample 522, line 2 2086 for the 1st water pixel; sample 398, line 1760 for the 2nd water pixel; and sample 234, line 1480 for the 3rd water pixel. Their corresponding spectral profiles are Figures 1b, 1c, 1d, and 1e. Figure 1a Figure 1b Figure 1c Figure 1d Figure 1e Figure 2 Perform the Radiometric Correction (ELC) After you exported/saved the five image (pixel) spectral profiles (.sli) and copied the five in situ spectra (.txt) to your own folder/drive, it is ready to perform the radiometric correction from WorldView-2 (WV2) image BV to reflectance through ELC. To do so, go Toolbox to press Radiometric Correction, then press Empirical Line Compute Factors and Correct (if you already have existing ELC factors, press Empirical Line Correct Using Existing Factors). Checking the msr4c1p001.img from Empirical Line Input File panel and do Spatial and Spectral Subsets if necessary, then click OK to pop-up an Empirical Line Spectra panel Figure 3). Fill the panel by 3 importing five image/pixel spectra (five .sli files) by pressing Data Spectra: Import Spectra and corresponding five in situ spectra (five *.txt files) by pressing Field Spectra: Import Spectra for simulating a regression/calibration model for converting WV2 BV image data to reflectance format data. To fill the panel, first import the five Data Spectra (image/pixel spectra) by clicking Import Spectra and then import five Field Spectra by click the Import Spectra. While you click the Import Spectra, a Data Spectral Collection panel pops up for your selecting import spectra. You can press Import/from Spectral Library file…to select the five image/pixel spectra from Spectral Library Input File panel one by one (.sli). When you click a .sli (e.g., sand1.sli) from the panel, an Input Spectral Library panel pops up (Figure 4). Select the spectrum and click OK to input the spectrum into Data Spectra Collection panel. If the image/pixel spectra are not in Spectral Library Input File, press Open/Spectral Library… (from bottom) to find the saved image spectrum (.sli) to click it to input into Spectral Library Input File (if you select/save the image spectra and didn’t exit the ENVI, the image/pixel spectra should be already in Spectral Library Input File). So far, the Data Spectral Collection panel looks like Figure 5 (with five image/pixel spectra). Press Select All then press Apply to fill the five image spectra into the Empirical Line Spectra panel. Close the Data Spectral Collection then back to the Empirical Line Spectra panel to click Import Spectra for importing Field Spectra. To do the same thing as for importing the image/pixel spectra to import the five in situ spectra (sand1.txt, sand2.txt, water1.txt, water2.txt and water3.txt). Note this time you need pressing Import/from ASCII file… to import the five in situ/field spectra (Figure 6) because the five in situ spectra are saved in ASCII (you can open and check them with Notepad). After you import the five pairs of image/field spectra, you can make pairs for the image spectra and in situ spectra by selecting Data Spectra and Field Spectra then pressing Enter Pairs similar to Figure 7. From the filled panel, click OK and then to fill a small window (Empirical Line Calibration Parameters), just pop-up, with you output filename (for your calibrated image file, e.g., msr4c1p001_elc.img) and output calibration filename (.cff) (see Figure 8), then Click OK to perform the ELC. 4 Figure 3 Figure 5 Figure 7 Figure 4. Figure 6 Figure 8. 5 So far, you can find that an ELC calibrated image file (in reflectance) has been created from Data Manager panel and automatically been displayed in the View. You can press the ELC image (i.e., calibrated WV2 imagery) by clicking Change RGB Bands… in Layer Manager in the left of the View to display its color composite image you prefer. You may find its spectral profile of the calibrated image with vertical axis of Digital Number (0−2047) and actual reflectance is [0-1]. Therefore, we need editing the calibrated header file (.hdr). To do so, close the calibrated image and the original WV2 image then go to the folder where the calibrated header file (msr4c1p001_elc.hdr) was saved to click the .hdr file where you can find two lines: z plot range = {0.00, 2047.00} z plot titles = {Wavelength, Digital Number}. Change them to z plot range = {0.00, 1.00} z plot titles = {Wavelength, Reflectance} and save the .hdr file. Then you can open the msr4c1p001_elc.img and check the spectral profile with vertical axis of Reflectance and its value from 0 to 1 (by pressing right button Auto Scale Y-Axis in the Spectral profile to rescale the vertical axis). Part III - Compare the Calibrated Image with the Original One Now you need comparing the Empirical Line Calibrated image with the original WV2 data. Please close all image windows. Open two images (original WV2 image: msr4c1p001.img and Empirical Line Calibrated image: msr4c1p001_elc.img) in the two new views (either two vertical views or two horizontal views). After display the two composite images, press Views/Link Views to make the two composite images linked (either Geo Link or Pixel Link) by clicking View 1 and View 2 and then click OK in the Link Views to close it. Click Spectral Profile icon after clicking View 1 and View 2 to pop up the two Spectral Profile plots and then type in the coordinate as (2355, 873) into and press Enter. Now you can find that the two healthy vegetation curves show up in each plot. You can use the Crosshair cursor to locate a sea water pixel that you think it is representative (note no data for the large low right area of the image), and with the same procedure to create spectral profile for an urban pixel from both images. You can see the profiles more clearly when you modify the profile chart option for Y axis by pressing Right-button/Auto Scale Y-Axis in the Spectral Profile. Questions: 1. Display and compare differences of the spectral profiles between the ELC image and the original image for (Hint: You need to display, describe the differences, copy/paste spectral profiles of ELC image and original image): - Healthy Vegetation (sample 2290, line 620) - Urban (sample 110, line 151) - Sea Water (sample 1246, line 455) 2. Do you think the ELC method corrected the atmospheric effect very well? What errors might be included in your ELC method (Hint: Spatial and temporal errors, not perfectly matching between image spectra and in situ/field spectra; white point(s) is not whitest and black point(s) is not blackest, leading to negative or >1 ELC pixel values)? Evaluate your ELC 6 result (Hint: Check ELC image profiles patterns and percentages of negative and > 1 ELC pixels). 3. Which features (targets, land cover types) have a possible negative reflectance or the reflectance larger than 1 per band (Hint: you might want to use Display/Profiles)? Why do you think your result has the reflectance less than 0 or larger than 1 (Hint, Not so whitest and blackest for image spectra for simulating calibration models)? How can you fix the problem (Hint: Either re-do ELC or assign all unreasonable ELC pixel values within 0 – 1)? ________________________________________ References Jensen, John R., 2015, 4th edition, Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall: Upper Saddle River, NJ 07458, 623 pages. 7 Questions: 1. Display and compare differences of the spectral profiles between the ELC image and the original image for (Hint: You need to display, describe the differences, copy/paste spectral profiles of ELC image and original image): - Healthy Vegetation (sample 2290, line 620) - Urban (sample 110, line 151) - Sea Water (sample 1246, line 455) 2. Do you think the ELC method corrected the atmospheric effect very well? What errors might be included in your ELC method (Hint: Spatial and temporal errors, not perfectly matching between image spectra and in situ/field spectra; white point(s) is not whitest and black point(s) is not blackest, leading to negative or >1 ELC pixel values)? Evaluate your ELC result (Hint: Check ELC image profiles patterns and percentages of negative and > 1 ELC pixels). 3. Which features (targets, land cover types) have a possible negative reflectance or the reflectance larger than 1 per band (Hint: you might want to use Display/Profiles)? Why do you think your result has the reflectance less than 0 or larger than 1 (Hint, Not so whitest and blackest for image spectra for simulating calibration models)? How can you fix the problem (Hint: Either re-do ELC or assign all unreasonable ELC pixel values within 0 – 1)? Lecture #3 Electromagnetic Radiation Principle and Radiometric Correction Will cover relevant contents of Chapter 6 in the text book: Introductory Digital Image Processing Also referred to http://www.cnr.berkeley.edu/~gong/textbook: Chapter 5 Outline 1. Electromagnetic radiation principle 2. EMR interaction with atmosphere/terrain 3. Atmospheric transfer 4. Correcting remote sensing system detector error 5. Remote sensing atmospheric correction 6. Correcting for slope and aspect effects 7. Summary 8. Lecture #3, complementary Electromagnetic Radiation Principles and Radiometric Correction • Remote sensing systems do not function perfectly. • Earth’s atmosphere, land, and water are complex and do not lend themselves well to being recorded by remote sensing devices • Error with the data acquisition process can degrade the quality of the remote sensor data collected. • Two most common types of error encountered in remotely sensed data are radiometric and geometric. Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information in a geographic information system (GIS) or spatial decision support system (SDSS). Radiometric correction attempts to improve the accuracy of spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. • • Electromagnetic Radiation Principles and Radiometric Correction • Radiometric and geometric correction of remotely sensed data are normally referred to as preprocessing operations because they are performed prior to information extraction. • Image preprocessing hopefully produces a corrected image that is as close as possible, both radiometrically and geometrically, to the true radiant energy and spatial characteristics of the study area at the time of data collection. • There are two types of errors: internal and external (radiometric and geometric) errors that must be identified to correct the remotely sensed data. Electromagnetic Radiation Principles and Radiometric Correction • 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 errors 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. Radiometric Correction of Remote Sensor Data • Radiometric correction requires knowledge about electromagnetic radiation principles and what interactions take place during the remote sensing data collection process. • To be exact, it also involves knowledge about the terrain slope and aspect and bi-directional reflectance characteristics of the scene. • Therefore, this lecture will review fundamental electromagnetic radiation principles. It then discusses how these principles and relationships are used to correct radiometric distortion in remotely sensed data caused primarily by the atmosphere and terrain. Electromagnetic Energy Interactions Energy recorded by remote sensing systems undergoes fundamental interactions that should be understood to properly preprocess and interpret remotely sensed data. The energy: • is radiated by the Sun, • travels through the vacuum of space at the speed of light, • interacts with the Earth’s atmosphere, • interacts with the Earth’s surface, • interacts with the Earth’s atmosphere once again, and • finally reaches the remote sensor, where it interacts with various optics, filters, film emulsions, or detectors. 1. Electromagnetic Radiation Principle Electromagnetic Radiation Models • To understand • how electromagnetic radiation is created, • how it propagates through space, and • how it interacts with other matter, • it is useful to describe the processes using two different models: - the wave model - the particle model. Wave Model of EM Energy An electromagnetic wave is composed of electric and magnetic vectors that are orthogonal to one another and travel from the source at the speed of light (3 x 108 m s-1). The Wave Model of Electromagnetic Energy     Frequency: the number of wavelengths that pass a point per unit time Wavelength: the mean distance between maximums (or minimums) Common units: micrometers (µm) or nanometers (nm). One cycle per second is termed one hertz (1Hz) Wave Model of Electromagnetic Energy The relationship between the wavelength, , and frequency, , of electromagnetic radiation is based on the following formula, where c is the speed of light: c = λ ⋅v c v= λ c λ= v Note that frequency, , is inversely proportional to wavelength,  The longer the wavelength, the lower the frequency, and vice-versa. Wave Model of Electromagnetic Energy Sources of Electromagnetic Energy   The Sun yields a continuous spectrum of EM energy This process produces a large amount of short wavelength energy (e.g., from 0.4 - 0.7 µm; blue, green, and red light) Interacts with the atmosphere and surface materials. (reflect, absorb)  Absorption: absorb the short wavelength energy and then re-emit it at a longer wavelength  Stephen Boltzmann Law  The total emitted radiation (M) from a blackbody is proportional to the fourth power of its absolute temperature. This is known as the Stefan-Boltzmann law and is expressed as: M = T4    where  is the Stefan-Boltzmann constant, 5.6697 x 10-8 W m-2 K-4. T: absolute temperature (in degree Kelvin) The greater the T, the greater the amount of radiant energy exiting the object The temperature 0oC (in the common Celsius scale) corresponds to 273K Wien’s Displacement Law  Wien’s Displacement Law is used for computing dominant wavelength (λmax) as: λmax = k / T  where k is a constant equaling 2898 µm K, and T is temperature in degrees Kelvin.  The Sun approximates a 6,000 K blackbody, therefore its dominant wavelength is: 0.483 µm = 2898 µm ˚K / 6000 ˚ K  T determines the wavelength.  Therefore from the (λmax ) information, T can be calculated Blackbody Radiation Curves    Blackbody radiation curves for the Sun: temperature approximate 6,000 K For Earth: 300 K As the temperature of the object increases, its dominant wavelength shifts toward the short wavelength portion of the spectrum. Particle Model of EM Energy   Quantum theory of electromagnetic radiation: energy is transferred in discrete packets called quanta or photons. The relationship between the frequency of radiation and the quantum is: Q=hν  where Q is the energy of a quantum measured in Joules (J), h is the Planck constant (6.626 x 10-34 J s1), and ν is the frequency of the radiation. Particle Model of EM Energy The Electromagnetic energy travel equation :  = c/v, v = c/  By substituting Q for h  (Q = h ν = hc/λ), the wavelength is associated with a quantum of energy as:  = h c / Q, or Q=hc/ Thus, the energy of a quantum is inversely proportional to its wavelength, i.e. the longer the wavelength involved, the lower its energy content. (Linked to Wien’s Displacement Law) 2. EMR Interaction with Atmosphere/terrain Atmospheric Interactions • Scattering • Absorption Atmospheric Scattering Electromagnetic radiation is propagated through the Earth's atmosphere almost at the speed of light in a vacuum. • Unlike a vacuum in which nothing happens, however, the atmosphere may affect • speed of radiation • wavelength • intensity • spectral distribution, • direction. Atmospheric Scattering The type of scattering is a function of: • the wavelength of the incident radiant energy, and • the size of the gas molecule, dust particle, or water vapor droplet encountered. Atmospheric Scattering Reflection: the direction predictable Scattering: direction unpredictable Based on wavelength of incident radiant energy, the size of the gas molecule, dust particle, or water vapor droplet essentially three types of scattering: • Rayleigh, • Mie, and • non-selective scattering. Rayleigh Scattering  Rayleigh scattering occurs when the diameter of the matter (usually air molecules) are many times smaller than the wavelength of the incident electromagnetic radiation.  Rayleigh named after the English physicist  All scattering is through absorption and reemission procedure Rayleigh Scattering The amount of scattering is inversely related to the fourth power of the radiation's wavelength (λ-4).  For example, blue light (0.4 µm) is scattered 16 times more than nearinfrared light (0.8 µm).  Mie Scattering  Mie scattering: when essentially spherical particles present in the atmosphere with diameters approximately equal to the wavelength of radiation  For visible light, water vapor, dust, and other particles ranging from a few tenths of a micrometer to several micrometers in diameter are the main scattering agents. The amount of scatter is greater than Rayleigh scatter and the wavelengths scattered are longer.  Pollution also contributes to beautiful sunsets and sunrises, caused by Mie scattering. The greater the amount of smoke and dust particles in the atmospheric column, the more violet and blue light will be scattered away and only the longer orange and red wavelength light will reach our eyes. Non-selective Scattering • Non-selective scattering: when particles in the atmosphere several times (>10) greater than the wavelength of the radiation. • All wavelengths of light are scattered, not just blue, green, or red. Thus, water droplets scatter all wavelengths of visible light equally well, causing the cloud to appear white (a mixture of all colors of light in approximately equal quantities produces white). • Scattering can severely reduce the information content of remotely sensed data to the point that the imagery looses contrast and it is difficult to differentiate one object from another. Color of the sky • Two questions: • Why is the sky blue? • When the air is clear the sunset will appear yellow Color theory Additive describes the resultant color when light is mixed, primary colors: red, green, and blue to form complementary colors: cyan, magenta, and yellow Subtractive describes the resultant color when dye is mixed, complementary colors : cyan, magenta, and yellow Light mixing follows this Dye/pigment mixing follows this Use for paint/filter Visual display Color photograph is based on subtractive mixing of complementary colors TV screen Monitor White = Blue+Green+Red Subtractive color = noncolors Black Black = Yellow+Magenta+Cyan White = no color Color of the sky • Why is the sky blue? A clear cloudless day-time sky is blue because molecules in the air scatter blue light from the sun more than they scatter red light • When the air is clear the sunset will appear yellow When we look towards the sun at sunset, we see red and orange colors because the blue light has been scattered out and away from the line of sight http://math.ucr.edu/home/baez/physics/General/BlueSky/blue_sky.html Atmospheric Absorption • Absorption is the process by which radiant energy is absorbed and converted into other forms of energy. An absorption band is a range of wavelengths (or frequencies) in the electromagnetic spectrum within which radiant energy is absorbed by substances such as water (H2O), carbon dioxide (CO2), oxygen (O2), ozone (O3), and nitrous oxide (N2O). • The cumulative effect of the absorption by the various constituents can cause the atmosphere to close down in certain regions of the spectrum. This is bad for remote sensing because no energy is available to be sensed. Absorption   In certain parts of the spectrum such as the visible region (0.4 - 0.7 µm), the atmosphere does not absorb all of the incident energy but transmits it effectively. Parts of the spectrum that transmit energy effectively are called “atmospheric windows”. • The atmosphere essentially “closes down” in certain portions of the spectrum while “atmospheric windows” exist in other regions that transmit incident energy effectively to the ground. It is within these windows that remote sensing systems must function. • The combined effects of atmospheric absorption, scattering, and reflectance reduce the amount of solar irradiance reaching the Earth’s surface at sea level. Absorption  Absorption occurs when the energy transformed into heat motion and re-radiated at a longer wavelength.  Transmission is inversely related to the thickness of the layer (of atmosphere).  Certain wavelengths of radiation are affected far more by absorption than by scattering.  This is particularly true of infrared and wavelengths longer than visible light. Absorption of the Sun's Incident Electromagnetic Energy in the Region from 0.1 to 30 µm by Various Atmospheric Gases An atmospheric window Terrain Energy-Matter Interactions We begin with the simple radiation budget equation, which states that the total amount of radiant flux in specific wavelengths () incident to the terrain ( Φ i ) must be accounted for λ • by evaluating the amount of radiant flux reflected from the surface ( Φ reflected λ ), • the amount of radiant flux absorbed by the surface ( Φ absorbed λ ), and • the amount of radiant flux transmitted through the surface ( Φ transmitted λ ): Φ iλ = Φ reflected λ + Φ absorbed λ + Φ transmitted λ Hemispherical Reflectance, Absorption, and Transmittance The Hemispherical reflectance () is defined as the dimensionless ratio of the radiant flux reflected from a surface to the radiant flux incident to it: Φ ρλ = reflected Φ iλ Hemispherical transmittance () is defined as the dimensionless ratio of the radiant flux transmitted through a surface to the radiant flux incident to it: Φ transmitted τλ = Φ iλ Hemispherical absorptance () is defined by the dimensionless relationship: Φ α λ = absorbed Φ iλ Hemispherical Reflectance, Absorption, and Transmittance These radiometric quantities are useful for producing general statements about the spectral reflectance, absorptance, and transmittance characteristics of terrain features. In fact, if we take the simple hemispherical reflectance equation and multiply it by100, we obtain an expression for percent reflectance ( ρ λ ): % ρλ = % Φ reflected λ Φ iλ ×100 This quantity is used in remote sensing research to describe the general spectral reflectance characteristics of various phenomena. Reflectance  Reflectance is the process whereby radiation “bounces off” an object.  various types of reflecting surfaces: • Specular reflection • Diffuse reflection • Lambertian surface Reflectance • Specular reflection(a): smooth (i.e. the average surface profile is several times smaller than the wavelength of radiation). • Diffuse reflection (b): rough, the reflected rays go in many directions. • Lambertian surface (d) the radiant flux leaving the surface is constant for any angle of reflectance to the surface normal. 3. Atmosphere Transfer Radiance The concept of radiance (Lλ) leaving a specific projected source area (A) on the ground, in a specific direction (θ), and within a specific solid angle (Ω) The Lλ is measured in watts per meter squared per steradian (W m-2 sr -1 µm-1). We are only interested in the radiant flux in certain wavelengths (Φλ) leaving the projected source area (A) within a certain direction (θ) and solid angle (Ω) Atmospheric transfer • Radiance (LT) from paths 1, 3, and 5 contains intrinsic valuable spectral information about the target of interest. • The path radiance (Lp) from paths 2 and 4 includes diffuse sky irradiance or radiance from neighboring areas on the ground. • This path radiance generally introduces unwanted radiometric noise in the remotely sensed data and complicates the image interpretation process. Atmospheric transfer Path 1 contains spectral solar irradiance ( Eo ) that was attenuated very little before illuminating the terrain within the IFOV. Notice in this case that we are interested in the solar irradiance from a specific solar zenith angle ( θ o ) and that the amount of irradiance reaching the terrain is a function of the atmospheric transmittance at this angle ( Tθ ). λ o If all of the irradiance makes it to the ground, then the atmospheric transmittance ( Tθ ) equals one. If none of the irradiance makes it to the ground, then the atmospheric transmittance is zero o Atmospheric transfer Path 2 contains spectral diffuse sky irradiance ( Ed ) that never even reaches the Earth’s surface (the target study area) because of scattering in the atmosphere. Unfortunately, such energy is often scattered directly into the IFOV of the sensor system. λ It contains much unwanted diffuse sky irradiance that was inadvertently scattered into the IFOV of the sensor system. Therefore, if possible, we want to minimize its effects. Atmospheric transfer Path 3 contains energy from the Sun that has undergone some Rayleigh, Mie, and/or nonselective scattering and perhaps some absorption and reemission before illuminating the study area. Thus, its spectral composition and polarization may be somewhat different from the energy that reaches the ground from path 1. Path 4 contains radiation that was reflected or scattered by nearby terrain ( ρλ ) covered by snow, concrete, soil, water, and/or vegetation into the IFOV of the sensor system. The energy does not actually illuminate the study area of interest. Therefore, if possible, we would like to minimize its effects. n Path 2 and Path 4 combine to produce what is commonly referred to as Path Radiance, Lp. Atmospheric transfer Path 5 is energy that was also reflected from nearby terrain into the atmosphere, but then scattered or reflected onto the study area. Atmospheric transfer The total radiance reaching the sensor is: 1 LS = ρ Tθ v (Eo∆λTθ o cos θ o ∆λ + Ed ) + L p π This may be summarized as: LS = LT + L p Atmospheric transfer Outline 1. Electromagnetic radiation principle 2. EMR interaction with atmosphere/terrain 3. Atmospheric transfer 4. Correcting remote sensing system detector error 5. Remote sensing atmospheric correction 6. Correcting for slope and aspect effects 7. Summary 8. Lecture #3, complementary 4. Correcting Remote Sensing System Detector Error Correcting Remote Sensing System Detector 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, water, or urban land cover) on the Earth’s surface. • 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. Random Bad Pixels (Shot Noise) Sometimes an individual detector does not record spectral data for an individual pixel. When this occurs randomly, it is called a bad pixel. When there are numerous random bad pixels found within the scene, it is called shot noise because it appears that the image was shot by a shotgun. Shot noise is identified and repaired using the following methodology. It is first necessary to locate each bad pixel in the band k dataset. A simple thresholding algorithm makes a pass through the dataset and flags any pixel (BVi,j,k) having a brightness value of zero (assuming values of 0 represent shot noise and not a real land cover such as water). Once identified, it is then possible to evaluate the eight pixels surrounding the flagged pixel, as shown below: Random Bad Pixels (Shot Noise) The mean of the eight surrounding pixels is computed using the equation and the value substituted for BVi,j,k in the corrected image: BVi , j ,k  8   ∑ BVi   = int  i =1  8    Shot Noise Removal a) Landsat Thematic Mapper band 7 (2.08 – 2.35 mm) image of the Santee Delta in South Carolina. One of the 16 detectors exhibits serious striping and the absence of brightness values at pixel locations along a scan line. b) An enlarged view of the bad pixels with the brightness values of the eight surrounding pixels annotated. c) The brightness values of the bad pixels after shot noise removal. This image was not destriped. 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. Line or Column Drop-outs 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 (BVi – 1,j,k) and succeeding line (BVi + 1,j,k) and assign the output pixel (BVi,j,k) in the drop-out line the average of these two brightness values: BVi , j ,k  BVi −1, j ,k + BVi +1, j ,k  = int   2   This is performed for every pixel in a bad scan line. The result is an image consisting of interpolated data every nth line that is more visually interpretable than one with horizontal black lines running systematically throughout the entire image. This same cosmetic digital image processing procedure can be applied to column drop-outs produced by a linear array remote sensing system. N-line Striping Noticeable lines (almost uniformly 20 brightness values greater than the other detectors for the same band) that are brighter than adjacent lines. This is referred to as n-line striping. 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. N-line Striping N-line Striping a) Original band 10 radiance (W m-2 sr-1) data from a GER DAIS 3715 hyperspectral dataset of the Mixed Waste Management Facility on the Savannah River Site near Aiken, SC. The subset is focused on a clay-capped hazardous waste site covered with Bahia grass and Centipede grass. The 35-band dataset was obtained at 2 × 2 m spatial resolution. The radiance values along the horizontal (X) and vertical (Y) profiles are summarized in the next figure. b) Enlargement of band 10 data. c) Band 10 data after destriping. d) An enlargement of the destriped data N-line Striping a). The radiance values along the horizontal (X) profile of the original band 10 radiance values in the previous figure. b). The radiance values along the vertical (Y) profile of the original band 10 radiance values in the previous figure. c). The radiance values along the vertical (Y) profile of the destriped band 10 radiance values. Note the reduction of the saw-toothed pattern in the destriped data 5. Remote Sensing Atmospheric Correction Why Atmospheric Correction?     Image radiometry can be affected by factors, such as system noise, sensor malfunction and atmospheric interference The purpose of atmospheric correction is to remove or reduce the effect of atmospheric interference on RS images. Atmospheric molecules and aerosols scatter solar radiance and ground reflected radiance (mostly affect λ1 ELC pixel values)? Evaluate your ELC result (Hint: Check ELC image profiles patterns and percentages of negative and > 1 ELC pixels). 3. Which features (targets, land cover types) have a possible negative reflectance or the reflectance larger than 1 per band (Hint: you might want to use Display/Profiles)? Why do you think your result has the reflectance less than 0 or larger than 1 (Hint, Not so whitest and blackest for image spectra for simulating calibration models)? How can you fix the problem (Hint: Either re-do ELC or assign all unreasonable ELC pixel values within 0 – 1)? Healthy vegetation: Sea water
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