Remote Sensing Lab 6: Geometric Correction

   Goals and Background: This lab was meant to familiarize me with geometric correction of images used in preprocessing before images can be studied for spatial-statistical information about observed phenomena. We used a USGS 7.5 minute raster of chicago in order to correct a Landsat TM image of the same area (image to map rectification), and we used a Landsat TM image of Sierra Leone to correct a distorted image of the same area (image to image registration).

   Methods:

Part 1 (Image to Map Rectification): Starting with a new 2D viewer in ERDAS Imagine, I opened the USGS Chicago DRG reference file. I then opened a new view and displayed the Image of the same area I intended to geometrically correct. I then made sure that the view of the image I wished to correct was highlighted. Next, I clicked on multispectral, then control points, opening the Set Geometric Model dialog. I now clicked on Polynomial, then okay, this correction being done with a first operation polynomial process. Next I clicked on image layer (new view), and okay. Navigating to the my personal folder I added the USGS image which I wished to reference, and clicked okay on the reference map information dialog. Going on, I accepted default model properties, and maximized the window in order to have more space to do the sensitive work of finding and moving matching points on the maps. I now deleted the current GCPs and created three new ones, selecting precisely the same place on the two images, then moving one of them while zoomed in extremely far while watching my RMS value decrease. After my total RMS was low enough for my standards, I clicked the display resample image dialog button, after which I specified my output location and specific name and accepted he other default parameters and clicked okay.

Part 2 (Image to Image Registration): Bringing both the image I wanted to correct and the reference image into a single viewer in ERDAS Imagine, I right clicked in the viewer and clicked swipe. I now used to slider to observe the difference between the images (the extent to which the one image was distorted). I now closed the viewer swipe window, then cleared the reference image from the viewer. I now clicked on multispectral, then control points, selecting polynomial, and setting my reference image file in the resulting windows before the main interface window. On one of the windows I observed the coordinate system the image was in, and on another I changed the polynomial order to 3. I now deleted the GCPs already present, and created 10 of my own, correcting as need be in order to get an adequate RMS before adding more GCPs automatically placed for good measure and to prevent the wrap tool error. I now saved and named the file and changed the resample method to bilinear interpolation.

   Results: 

Corrected Chicago

Corrected Sierra Leone

   Sources:
Data was supplied by my instructor. Satellite images are from Earth Resources Observation and Science Center, United States Geological Survey. Digital raster graphic (DRG) is from Illinois Geospatial Data Clearing House.

Remote Sensing Lab 5: LIDAR

   Goals and Background: This lab familiarized me with basic LIDAR processing, and had me make DTMs and DSMs, and an intensity image.
 
   Methods: 

Part 1: I copied the 40 individual LAS files from my department server into a personal folder for LIDAR processing. I then opened them all in a new 2 dimensional viewer ERDAS Imagine. I denied the software's request to make LOD. I then took a look at metadata in this software in order to familiarize myself with this dataset.

Part 2: I opened ArcMap and ArcCatalog. In ArcMap I right clicked on the top interface in order to turn on the LAS Dataset toolbar. I also clicked on customize, then extensions, and turned on 3D analyst, and spatial analyst. I also clicked on geoprocessing, then environments, to set my scratch and current workspace, afterward saving the ArcMap map file in my folder for future ease of work on this lab. Next, I right clicked on my folder in ArcCatalog, and created a new LAS dataset. I then named it and went into its properties in order to add all of my LAS tiles files, and to click on statistics, and then calculate. I then examined the statistics and compared them to other research for quality assurance purposes. Examining the metadata supplied with the data in an external .xml file, I found the horizontal and vertical coordinate systems and their respective units. With this information I set the coordinate systems of the dataset in ArcMap by right clicking on the dataset in the catalog and selecting properties.

I now dragged this correctly configured dataset from the catalog into the map. Loading a shapefile containing the boarders of the county as reference, I checked that the LIDAR tiles were located in the appropriate place. I now played with the LAS Dataset toolbar in, displaying the dataset in a variety of ways, also changing the classification settings in symbology layer properties and the selection to slope, elevation, and contour.

In order to create a profile of a bridge in Eau Claire, I clicked on the appropriate button on the toolbar, and positioned the resulting box over the bridge. I did the save with the 3D interactive profile function, and also used the two functions to explore other areas of the city.

Part 3: In this section I created a DTM and a DSM, hillshades of both, and an intensity image. Setting data to dots of elevation and first return, I began my DSM. I opened the LAS Dataset to Raster tool and set value field to elevation, cell type to maximum, void fill to nearest neighbor, sampling type to cell size and its value to 6.56168, and saved it in the right place with a descriptive title. After displaying this new file I used the hillshade tool inputting the DSM file, and looked at this file.

Now I created a DTM by using the LAS Dataset to Raster tool again but changing settings to binning, natural neighbor, minimum, and cellsize with the same size as the DSM. Before this I made sure to set the data to ground, and to points of elevation. I again ran the hillshade tool to make another hillshade image..

Next I created the intensity image. In order to do this I changed my dataset to points of elevation, and first return, then running the LAS Dataset to Raster tool again. I set the parameters to intensity, average, and natural neighbor, and cell size with the same size. I now opened this image in ERDAS Imagine because it automatically enhances the display.

Results: 

DSM

DTM

DTM Hillshade

DSM Hillshade

Intensity Displayed in ERDAS Imagine

Sources:

Data was provided by my instructor. Lidar point cloud and Tile Index are from Eau Claire County, 2013. Eau Claire County Shapefile is from Mastering ArcGIS 6th Edition data by Margaret Price, 2014.

Remote Sensing Lab 4: Miscellaneous Image Functions

Goals and Background: The goal of this lab was to make me accustom to various functions aiding interpretation of aerial imagery in the ERDAS Imagine software. Specifically, cropping of an AOI (area of interest), and of a rectangular area using the inquire box, linking an aerial image to google earth for interpretation aid, resampling to be easier on the eyes in interpretation, using radiometric haze reduction functionality, image mosaicking using both express and pro methods for different results, and binary change detection using simple graphical modeling.

Methods: I used data that was given to me by my professor. Each different miscellaneous image function is in a different following part.

   

   Part 1 Section 1: I first made a subset image using the inquire box method. I opened an aerial image of Eau Claire area in a new viewer. then I clicked on raster to find the raster tools, after which I right clicked on the image to click on Inquire Box to show an inquire box. Clicking and moving as well as resizing by dragging the sides of the box I moved it over the Eau Claire city area. I now clicked apply on the inquire box viewer window. I now clicked on Subset and Chip, then Create Subset Image. I selected the output folder I had made earlier for this part of the lab and gave the new image a unique name, and then clicked from inquire box on the subset window. I now clicked okay, then after my process was finished I clicked dismiss on the process list window, then closed the process list window. I now brought in the finished subset image and screen captured it for use in my report. This subset image can be seen below in the results section.
   Part 1 Section 2: In this section I used a shape file of Eau Claire and Chippewa Counties in order to create a subset image from this AOI. In a new viewer containing the same aerial image as the last section, I brought in the shape file my instructor provided. To see this in the Select Layers to Add window I selected Files of type, then clicked on the shape file option in the drop-down menu. I next shift-clicked both counties' shape files in order to select both, changing them from a shade of blue to a bright yellow. Now I clicked on Home, then paste from selected object which created an area of interest around the shape files, denoted by dashed lines. Now I clicked File, then Save As - AOI Layer As and saved this AOI as a unique file name with a .aoi ending. I then opened the image in a new viewer and screen captured it for use in my report. This sunset image can be seen in my results section below.

   Part 2: In this part I created a higher spatial resolution image from a lower resolution image and a panchromatic image for easier interpretation. I opened the image I was given by my instructor in a new viewer, then clicked raster, pan sharpen, then resolution merge from the dropdown menu. In the resulting Resolution Merge window I opened the panchromatic image supplied in the High Resolution Input File area, and the multispectral low resolution image supplied by my instructor in the Multispectral Input File area. I next created a unique name for my output image and saved it in the output folder previously created in the Output File area of the Resolution Merge window. I next clicked the multiplicative and nearest neighbor radial buttons. Clicking okay, I created the new image and opened a new viewer to view it.

   Part 3: In this part I used the radiometric haze reduction technique to remove the haze from an image. After opening the image I was supplied with, I clicked on Radiometric, then Haze Reduction. I next browsed to my output folder in the resulting window, and entered a unique output image name. I then clicked okay, using all of the default parameters, then opened the image in a second viewer in order to see the difference the Haze Reduction algorithm had made.

   Part 4: In this part I linked google maps for interpretation help. I first opened the image I was provided in a new viewer. Next, I clicked on Google Earth, then connect to Google Earth. Now, with google earth opened, I clicked on Link GE to View, then Sync GE to View in order to have the scale and area being looked at synced on both windows.
   Part 5: In this part I resampled an image using both the Nearest Neighbor, and the Bilinear Interpolation methods. Both methods were performed using the same method, but with the method chosen differing. I first opened the image that I was provided, then clicked metadata to see that the spatial resolution was 30 meters. Next, I clicked Spatial, then Resample Pixel Size to open the resampling window. Inputing the same image I had opened earlier, I outputted the new image as a unique file name and in my output folder. Next, I changed the output cell size from 30 x 30 to 15 x 15. Clicking Square Cells and my resample method, I then accepted the default parameters and clicked okay.
   Part 6: In this part I practiced image mosaicking in both the express and pro functions. I first used express. I used two images that were capture in May 1995 by the Landsat TM satellite. Adding each image one by one I opened the Select Layers to Add window, then clicked multiple, and Multiple images in Virtual Mosaic, then made sure that in the Raster Options tab the Background Transparent option was checked. I then clicked okay. I repeated the same process for the next image, then seeing the two overlapped in the viewer.
   Part 6 Section 1: In this section I used mosaic express. I clicked raster, then mosaic, then mosaic express. Next I added the image I wanted stacked on top first to the area in the input tab, then the image I wanted on the bottom. Clicking on the output tab, I then specified my unique output file name, and specified the output folder I created previously. I clicked finish to create the simple mosaic.
   Part 6 Section 2: In this section mosaic pro was used. I selected mosaic, then mosaic pro in order to begin this process. I clicked the Add Images button, then found and selected the first image to import. I then clicked to the Image Area Options and clicked Compute Active Area. I now clicked okay. I next did the exact same thing for the second image. In order to synchronize the radiometric properties of the images I clicked on the color correction button, clicked use histogram matching, then clicked set and selected overlap areas for the matching method. I now clicked okay and okay on the windows. Next I clicked the overlap function icon. I set the method to overlay and clicked okay. I now ran the mosaic, saving the file in my output folder with a unique name.
   Part 7 Section 1: In this section I practiced binary change detection. I first displayed the images I was given in two different viewers. Next, I clicked raster, then functions, then two image functions. Now I deleted my first input file and second input file which I had been supplied with. Now, I changed the function from + to - for differencing. I set my output file to a unique name and inside my output folder then changed the layer on both to only layer 4 for simplicity sake, and clicked okay. I now investigated the histogram of the resulting image by opening it in a new viewer and clicking metadata. I now used the mean and standard deviation to find and delineate for my lab report the areas which substantially changed. To find this I multiplied the SD by 1.5, and added and subtracted them from the mean to find the larger and smaller values of cutoff.

Results: Resulting images are shown and labeled.

Histogram

Mosaic Express

Part 1 Section 1

Mosaic Pro

Part 1 Section 2

Reference:

Satellite images are from

Earth Resources Observation and Science

Center, United States Geological Survey

. Shapefile is from

Mastering ArcGIS 6

th

edition Dataset

by Maribeth Price, McGraw Hill. 2014.

Data used was given to me through department server access by instructor.  Available upon request with the permission of my instructor.

Satellite images are from Earth Resources Observation and Science Center, United States Geological Survey. Shapefile is from Mastering ArcGIS 6th edition Dataset by Maribeth Price, McGraw Hill. 2014.

Satellite images are from

Earth Resources Observation and Science

Center, United States Geological Survey

. Shapefile is from

Mastering ArcGIS 6

th

edition Dataset

by Maribeth Price, McGraw Hill. 2014.