Using LiDAR Data

Supervised Classification

After 5 attempts, sad to say, this is the best I could get. I'm not sure what to do to improve the results.

Unsupervised Image Classification

Colors were selected for contrast between the assumed land uses. The original image didn't provide me with many clues. I did a lot of assuming after that and ended with 6 catgories out of the original 15.

Rectification; uses and pitfalls

In GIS, image rectification can place an aerial image in a geographic coordinate system, and remove distortion. The image can then be placed on a map in the correct location and scale. The process involves placing control points on the image that correspond with points on a map layer with a known coordinate system. Placing the control points is critical to achieving useful results. Two pitfalls of rectification involve the placing of the control points. If it is not done accurately, or if the image lacks recognizable points that correspond to the map layer, the image will continue to contain some degree of distortion.

Module 3, Fort Worth

1. Provide a brief description of why the following features appear as they do in
this thermal infrared image:

Roads- Light shade; the surface is emitting energy absorbed during day time.

Natural and man-made vegetation- Dark shade; absorbing energy and using it rather than emitting it later.

Sidewalks and patios- Light shade; reflecting energy. Concrete will reflect even small amounts of thermal energy.

Storage sheds in back yards- Dark shade, probably the same material as the house roof shingles, which reflect during the day and cool rapidly at night.

Automobiles- Mostly dark, unless recently driven, which generates heat.

Bright spots on many of the roof tops- Light shade; vent locations from the plumbing and dryers. They are emitting heat.

Exorcise 2

1, Air field

2. golf course

3. major roads

4. baseball fields

5. boat docks

6. Bridge

7. residential subdivision

8. boats on the water

Many more of the objects would have been recognizable with some familiarity with the area.

There are very large buildings visible, but what they are would be hard to say with out a higher resolution.

SPOT image differences

The first and most obvious difference between the two photos is the clear separation of water and land. The color in the multi spectral photo helps define the shore line. The under water contours are also better defined. Vegetation is apparent in the ERDAS Viewer as a bright red which also delineates the urban area by the lack of color. I don't know why the red does not show up in the image below. All the parameters matched those in ERDAS.
The panchromatic photo is sharper and better defines the urban area. It's easier to pick out individual buildings.