GIS4035 - Remote Sensing and Photo Interpretation - Module 7

GIS4035 - Remote Sensing and Photo Interpretation - Module 6

.   I tried running the clipped aerial through several different filters in ERDAS obtained following results;

a.      11x11 LowPass – image was very blurry and difficult to interpret.

b.      3x3 Edge Enhance – image became very uniformly grey, and the lines became darker and quite pronounced.

c.       5x5 High Pass – Image became very dark, and only larger features became all grey in color. Very difficult to read.

d.      3x3 Haze Reduction – Fairly readable, but the dark lines become awfully thick on the left side of the image.

e.      3x3 Sobel – Image became monochromatic, and much too dark.

2.  After many attempts, I decided to use my initial ‘sharpen1’ file from the early stage of the assignment. Put the finishing touches on the photo in ArcMap

GIS4035 - Remote Sensing and Photo Interpretation - Module 5A

The initial raster was modified with ERDAS Imagine. A new column was added to the attribute table, that allowed for the automatic calculation of area covered by each color category. The layout as well as the essential map elements were later added in ArcMaps, and the image was exported as a JPEG.

GIS4035 - Remote Sensing and Photo Interpretation - Module 4

The idea behind this weeks exercise, was to verify the accuracy of the LULC classification done for last weeks project. We used google maps to verify the data that was previously classified only through studying the given aerial. The green points fall on to features that were classified accurately, while the red fall on to features that were misinterpreted.

Out of the total 30 points, 23 of them were accurately classified, for a total of 76.666% accuracy.

GIS4035 - Remote Sensing and Photo Interpretation - Module 3

This shapefile was created over an aerial photograph, and split into various features to correspond with the LULC classification system. All the different fields describe different land use, and land cover interpretations.

11 – Residential: The majority of the land used throughout the right half of the map seemed to be made up of suburban dwellings. Medium sized houses, with front and back yards that surrounded narrow streets. They were all confined within a single feature, and highly generalized.

12 – Commercial and Services: The more of the larger, square buildings with parking lots were labeled under Commercial and Services. Whey were mainly done so, due to their size, and proximity to a major highway.

13 – Industrial: The main feature that was reminiscent of a factory was located in the bottom right. The other two were classified so due to their size and shape.

14 – Transportation: Only one major landmark fell into this category, and that is the large highway that cuts through the aerial north-south.

43 – Deciduous Forest Land: There was a lot of forested patches of land located among the residential landmass. I assumed them to be mostly deciduous by the apparent “fluffiness” of the trees.

51 – Streams and Canals: The entire body of water on the left-hand side of the photograph was classified under this category. It was quite difficult to tell exactly what this feature was, and braking it down into more specific categories would prove very time consuming. Therefore, the more general category of “Streams and Canals” seemed appropriate.

52 – Lakes: There are three isolated bodies of water which are completely surrounded by land in the photograph. I grouped them all under lakes, as they are land locked and seemingly large enough.

54 – Bays and Estuaries: This category was given to three water features, which seem to flow into the major body of water. They are all short, and not likely to be rivers.

61 – Forested Land: This classification included every one of the overgrown islands located on the large body of water to the left hand side. Because the islands were clearly not used for anthropogenic purposes. It was difficult to decipher the exact vegetation.

GIS4035 - Remote Sensing and Photo Interpretation - Module 2

Of the two maps that we were to submit for this weeks assignment, the first one focused on defining tone and texture. We created polygons around features of our own choice, based on how dark they appeared, as well as how grainy they appeared, and organized each of them into five levels. The objective of the lab was to show understanding of what tone and texture are, and to be able to express it clearly with ArcMap.





The second part of the assignment focused on identifying features based on four distinct criteria: shape/size, shadow, pattern and association. Many of the features on this aerial could have been identified using more than one criteria, but for the purpose of this assignment we were not supposed to re-use the same features to fill the quote (3 features per each criteria.)

GIS 4035 - Module 6 - Spatial Interpretation

Process Summary Details

Exercise 1:

NOTES:

1.      Retrieving the data files from the website was at first difficult, until I used a proper browser and made sure that the Java extension was up to date. Otherwise, I did not run into too many problems when it came to downloading the image of Pensacola Bay. As instructed, I closed browser and used the image provided in the zipped folder of Module 6 content.

2.      I unzipped both files required for this task, and opened them in Erdas imagine by importing them according to instructions.

Exercise 2:

NOTES:

1.      I began by adding p011r061_nn80 to a main project in Erdas Imagine. When I applied the 3x3 low pass Kernel filter (using the Convolution tool), the image seemed overall brighter. The edges of individual features were not at all distinguishable, but larger ‘bulk’ features could be seen quite prominently. I repeated the same steps in the convolution tool, but instead filtered the image through a 3x3 high pass filter. Smaller individual features became a lot more defined and easy to see, but boundaries of larger areas became more difficult to define.

2.      I opened the same image in ArcMap, and used the Focal Statistics tool according to directions. The raster set created used the “Mean” statistical filter. It was filtered through the 7x7 Kernel (instead of 3x3 cells,) therefore the image came out looking less defined than before.

3.      Once again, I used the Focal Statistics tool to open the same image, this time through a 3x3 Kernel and with the statistical filter for “Range” which is often used to define edges in features by highlighting the difference in brightness between neighboring pixels and feature in question.

Exercise 3:

Write down every enhancement process that you run on this image, and describe any noticeable effects of each. (Consider this the most important part of the process summary – there should be a lot of detail here.)

NOTES:

1.      I came back to Erdas Imagine for the final task of this lab.

2.      I opened the l7_striping.img file as directed in the assignment. The image appeared visible and perfectly fine.

3.      I went into the raster tab and clicked on “scientific” under group.

4.      I clicked into Fourier Analysis and selected the Fourier Transform Editor.

5.      I opened the l7_striping.fft file and explored the image. It appeared as a several light lines of star-like images of varied radiance, stretching diagonally from the top to the bottom of the image.

6.      I scrolled all the way up on the image, and over to the center-top as directed.

7.      I used the wedge tool by selecting it, and clicking at the center of the upper-most star in the middle row and moving the mouse cursor over to expand the “V” shape. The first attempt created a very thin band. I figured that it would not be sufficient to mask the striping effect, so I clicked undo and tired again.

8.      The second time around, I attempted to create a much larger “V” shape, but it still came out slightly thinner than the one shown on the screen shot in the module 6 directions. I decided to keep it for the times being, and see what the striping might look like after application.

9.      Afterwards, I scrolled down to the very center of the image and selected the LowPass button. I held down the left mouse key over the center of the image, and extended a circle to the very edge of the screen. The resulting image was a very bright sphere; fading out at the edges, with two long triangular features extending from the center and out towards the edges.

10.  When I compared the finished product to the sample presented ion the assignment, I thought it to be quite similar.

11.  I saved as Fourier1.fft, and used the Run tool to create a new Img file. When I opened it in Erdas, the file seems to have been created successfully.

12.  Back in Erdas, under the Raster tab and Resolution group I selected Spatial-Convolution.

13.  I selected Fourier1.img as the input file and selected 3x3 Sharpen in the Kernel menu. I ran the filter. The Sharpen image seemed quite almost exactly the same, but the definition was slightly superior.

14.  I tried repeating the process and obtained following results when choosing the filter;

a.      11x11 LowPass – image was very blurry and difficult to interpret.

b.      3x3 Edge Enhance – image became very uniformly grey, and the lines became darker and quite pronounced.

c.       5x5 High Pass – Image became very dark, and only larger features became all grey in color. Very difficult to read.

d.      3x3 Haze Reduction – Fairly readable, but the dark lines become awfully thick on the left side of the image.

e.      3x3 Sobel – Image became monochromatic, and much too dark.

15.  After many attempts, I decided to use my initial ‘sharpen1’ file from the early stage of the assignment.

16.  I added the essential map elements, after importing the raster file into ArcMap saved my lab assignment.