Introduction to Remote Sensing Wednesday, September 27, 2017

Transcription

1 Lab 3 (200 points) Due October 11, 2017 Multispectral Analysis of MASTER HDF Data (ENVI Classic)* Classification Methods (ENVI Classic)* SAM and SID Classification (ENVI Classic) Decision Tree Classification (ENVI Classic) *Two ENVI 5 tutorials, Calibrating Images and Classification, are included among the materials linked below. These are for your reference only do you not need to do them. There is also a white paper about the Empirical Line Calibration for your reference. PDFs of these tutorials and the necessary data are available at: \\tejas.geo.utep.edu\geo4336\assignments\lab3 Please copy (do not move) any material you need from this location into your own directory on \\tejas.geo.utep.edu\geo4336. Note that for large datasets, it may be beneficial to work off of the local hard drive on the workstation you are logged in to, rather than working off of the network drive. You should clean up your directory after you are finished with the lab by removing any large data files you no longer need. Please do this so that we do not fill up the server. Also clean up the workstation you are working on, keeping in mind that any files on the local hard drive will not be accessible to you from anywhere else. Be careful when cleaning up your data, however, to not delete any of your own work that you want to keep! Follow the instructions in the tutorials. As you go, you will be instructed on how to perform various functions with ENVI, including producing various image products. What you will need to turn are short (1 page maximum) summaries (in PDF format) of what you learned, illustrated by representative images of what you have done. Be sure to answer any questions posed in the instructions in your write-up. Make sure your figures are clearly labeled and captioned so I know what I am looking at and that you refer to them as figures in your write-up text. DO NOT TURN IN ANY ENVI FORMAT FILES! Do this separately for each tutorial, i.e. I expect one summary PDF document for each separate tutorial. One method for including images in your summaries is to export what you want out of ENVI as a JPEG file and paste it into your Word file. When you are done, you can output/print your Word file as a PDF. To avoid making ridiculously large PDFs, please ensure that your images are as small as possible (in terms of disk space). There are several ways to accomplish this. For example, when you save your image from ENVI using the Save Image As menu option, you are presented with a dialog box. At the top of the dialog box is an option to Spatially Subset. This will allow you to select just a small portion, rather than the entire image to save. In addition to subsetting, at the bottom of the dialog box, where you select the place to save and the filename, there are two options, one for JPEG compression and the other for a zoom factor. For more details on how to use these features, see the ENVI online help (and/or just experiment with the program). Submission of Work: Before submission, make yourself a folder called lab3_<yourlastname> and stick the file(s) you are turning in inside of it. Place a copy of this folder into the following directory: 1

2 \\tejas.geo.utep.edu\geo4336\dropbox Note that the DROPBOX is write-only, so you will not be able to see what is inside it, and you cannot retrieve anything you put in there (just like an old-fashioned mailbox). So be sure you have everything right before you submit, and be sure to keep copies of what you turn in for yourself! Do not turn in individual, loose files, and please follow the naming convention specified above for your folder. Also, do not turn in any ENVI format files! I will delete anything and everything that does not follow these instructions. ADDITION TO THE TUTORIAL INSTRUCTIONS: In the Multispectral Analysis of MASTER HDF Data tutorial, one of the tasks (on page 5) is to do an Empirical Reflectance Calibration. ENVI implements three types of empirical reflectance calibrations: Internal Average Reflectance (IAR), Flat Field, and Empirical Line. The tutorial asks you to do the Flat Field method. I would like you to also experiment with the other two methods. Below are some basic summaries of what these methods do: Raw measurements of electromagnetic radiation at optical wavelengths (i.e. imaging systems in the UV-VIS, VNIR, SWIR, TIR, etc.) are typically given as unitless digital counts or digital numbers (DN). These values are stored as numbers (for an 8-bit system) at each pixel location in the resulting image cube. Sensor calibrations are used to map those unitless DN into values with units of spectral radiance at the sensor (W m-2 str -1 m -1 ) by applying calibration coefficients. For a linear calibration model, this amounts to multiplying by a gain value and then adding an offset value: radiance gain( DN) offset (1) However, to use remotely sensed measurements of electromagnetic radiation, we must go further than just applying something like Equation (1). We must also (a) compute radiance at the surface (via atmospheric correction) and (b) then compute percent reflectance at the surface (via reflectance calibration). Once we have surface reflectance, we can meaningfully compare reflectance spectra extracted from the processed image cube with field or spectral library reflectance spectra. There are sophisticated methods that do systematic, physics-based atmospheric corrections that quantitatively remove the effects of atmospheric absorption and scattering. These methods use atmospheric and radiative transfer models coupled with climatological data to compute radiance-at-the-surface values from radiance-at-the-sensor values. (The result then needs to be further processed using some sort of reflectance calibration to finally yield surface reflectance.) ENVI has the capability to interface with some of these atmospheric correction algorithms (e.g. FLAASH, ATREM, etc.), but they require special licensing and ancillary data (e.g. weather and climate data) that is not always available. There are, however, three simple algorithms implemented in ENVI that do both a relative (i.e non-systematic) atmospheric correction as well as a reflectance calibration. As such they take radiance-at-the-sensor 2

3 values and compute surface reflectance values. These empirical reflectance calibration algorithms are: Internal Average Relative (IAR) Reflectance, Flat Field, and Empirical Line. A. Internal Average Relative Reflectance (IAR) Calibration This method is used to normalize image spectra by dividing the image spectrum of each uncorrected pixel by a mean image spectrum taken from the entire, uncorrected image. Since the atmosphere should be fairly uniform across the scene, what you will be normalizing out in this fashion is the average effect of the atmosphere. This method may be your only option in areas where no ground measurements are available and little is known about the scene. 1. Select Basic Tools> Calibration Utilities> IAR Reflectance 2. Select your input file (i.e. your image) and click OK. 3. When the IARR Calibration Parameters dialog box appears, indicate what to name your output file. Make sure to also indicate where to put it (click the Choose button). 4. Click OK to perform the IAR Reflectance calibration. B. Flat Field Calibration This method uses a normalization target you pick in your image as a reference. The normalization target should be a spatially- and spectrally-homogenous (but not necessarily contiguous) area that you assume should have flat reflectance spectrum after the calibration is complete. Note that this does not mean it has a flat spectrum before the calibration (in fact, it probably shouldn t) it just means that you are assuming the spectrum is supposed to be flat after the calibration! Basalt and quartz are examples of spectrally featureless materials (i.e. they have flat reflectance spectra, at least in the VNIR) that are often used as normalization targets. Computationally, this calibration is done by dividing the uncorrected image spectrum of each pixel in the image by the average, uncorrected image spectrum of the normalization target area(s). The end result for pixels in the flat field area(s) should be flat reflectance spectra. The mathematical transformation used to flatten these spectra is applied to all pixels in the image to remove the effects of the atmosphere and to do the conversion to reflectance. So, the end result for every other pixel in the scene (that doesn t represent a spectrally flat feature) should be an atmospherically-corrected reflectance spectrum. 1. Define an ROI (region of interest) over the area in your image that you believe should have a flat spectrum in the corrected image. Note that in the uncorrected image it will not have a flat spectrum! That is sort of the point! 2. Select Basic Tools> Calibration Utilities> Flat Field. 3

4 3. When the Flat Field Calibration Input File dialog box appears, select your input file (i.e. your image) and click OK. 4. When the Flat Field Calibration Parameters dialog box appears, select the average spectrum to use for the Flat Field Calibration by clicking on the desired ROI name (the one you defined in step 1) in the column labeled Select Calibration from Regions. 5. Indicate what to name your output file. Make sure to also indicate where to put it (click the Choose button). 6. Click OK to start the Flat Field Calibration C. Empirical Line (EL) Calibration (see also the included white paper for more information) This method requires a priori knowledge (e.g. ground control). First, at least one normalization target area is selected. For each normalization target selected, you must have access to reference reflectance spectrum representative of the surface composition of that target. This ancillary spectral information can be obtained from field- or laboratory-measured reflectance spectra of actual surface material or from reflectance spectra of similar material in a spectral library. Regardless of where it comes from, this reference reflectance spectrum is assigned to the normalization target area to indicate what its corrected reflectance spectrum should look like. For each normalization target, ENVI then calculates the mathematical transformation needed to convert between the uncorrected image spectrum and the user-defined reference reflectance spectrum. The resulting parameters (determined from all the normalization targets) are used to constrain a mathematical model that is applied to correct the entire image. Computationally, this is similar to the flat field correction except here we are not assuming the normalization target has a simple, flat spectrum. We are also able to specify multiple, spectrally-distinct normalization targets. 1. Select Basic Tools> Calibration Utilities> Empirical Line> Compute Factors and Calibrate. 2. When the Empirical Line Input File dialog box appears, select your input file (i.e. your image) and click OK. 3. In the Empirical Line Spectra dialog box, click on the Data Spectra: Import Spectra button. You can then import your reference spectra from a plot, profile, spectral library, ROI, or ASCII file. In this case you will be importing from an ROI (see Step 4 of the Flat Field correction procedure above). You will have to think carefully about where in your image to use as your normalization target(s) because later on you will need to make a guess as to what they are made of (see Step 4 below). 4. Click on the Field Spectra: Import Spectra button. Here is where you will input a reference reflectance spectrum representative of what the corrected normalization target s reflectance spectrum should look like in the calibrated image. In this case, import from the built-in ENVI spectral libraries (the 4

5 USGS or JPL Spectral Libraries are probably best). Hopefully, you thought carefully about your choice of normalization targets (see Step 3) because you now need to make a guess of what they are made of in order to pick a reasonable reference reflectance spectrum to use. 5. Pair your data spectra and field spectra by clicking on the lists in the dialog box you are presented with. 6. Click OK to perform the Empirical Line Calibration. ADDITIONAL QUESTIONS TO ANSWER: The performance of the above empirical reflectance calibration methods will vary depending on the scene. In each case (at least theoretically), the output image will show reflectance at the surface and should be suitable for further spectral analysis and interpretation. Your results may vary, though! Your results are only as good as the method being used and the quality of the normalization targets you choose! You can assess this by collecting random image spectra from the calibrated images and comparing them to library spectra. (Note, however, that you will have to make assumptions about what the surface cover in your scene is made of. Also, note that you should not pick the normalization target areas you used to make this assessment pick outside of them or you will be guilty of circular reasoning!) Try this for each of the methods, and make a rough assessment of which relative calibration method may work best. Do you think any of these are as good as what ATREM or some other physics-based atmospheric correction can produce? Also think about the similarity in shape of the raw image spectra (for any pixel in the uncorrected scene) to the blackbody spectrum for the Sun. Is there a similarity? Why? Is there any similarity after doing the corrections? Why or why not? 5