Remote Sensing Lab 4

Introduction to Lab 4: Miscellaneous Image Functions

This seven part lab was designed to acquaint us students to various functions and processes that can be performed on aerial images. There were multiple objectives for this lab, including: selecting and clipping an area of interest from a larger satellite image, learning about optimization of spatial resolution, and practice with linking a satellite image to Google Earth as ancillary information. This lab also served as an introduction to binary change detection, image mosaicking, common resampling methods, and various radiometric enhancement techniques such as haze reduction.

Methods

Aerial images and data was provided for us.  

Part one of the lab was to create a subset of the Eau Claire area from a larger satellite image using first the Inquire Box method, and then by delineating using a shapefile (see Results Figure 1 under Results).

In part two, the spatial resolution of an image was increased using image fusion pansharpening. An aerial photo of Eau Claire and Chippewa counties from the year 2000 with a resolution of 30 meters was pansharpened using a resolution merge with a 15 meter panchromatic image. I used a multiplicative method and a nearest neighbor resampling technique.

In part three, I removed haze from an aerial photo of Eau Claire using the Haze Reduction tool under radiometric enhancement techniques in ERDAS Imagine.

In part four I opened Google Earth through ERDAS Imagine and linked it to an aerial image of Eau Claire to use as ancillary information.

For part five, an aerial photo of Eau Claire with a resolution of 30 meters was resampled to 15 meter resolution by using the nearest neighbor method, and then again using bilinear interpolation. Both of these methods were under “Resample Pixel Size” under the Spatial raster tool.

Part six focused on image mosaicking. Two compatible rasters were brought into ERDAS Imagine (see Figure A below) and mosaicked first through the use of the Mosaic Express function and then through the use of Mosaic Pro. See Results Figure 3 to view the comparison.

Fig. A Two rasters in the process of being mosaicked with the Mosaic Express tool.

For part seven I created a difference image to highlight change that has occurred in Eau Claire and four neighboring counties by comparing a 1991 image to a 2011 image using Two Image Functions and input operators interface under the Functions raster tool. I ascertained the cutoff threshold points by adding the mean to the standard deviation value x 1.5. I drew these values onto the histogram, as you can see in figure B below.

Fig. B The histogram from part seven labelled with the cutoff threshold values.

Using the equation ΔBVijk = BVijk(1) – BVijk(2) + c and Model Maker, I created a model to subtract the 1991 image from the 2011 image (figure C below).

Fig. C 

I then created a second model to command Imagine to show all pixels with values above the no change threshold value and mask out those that are below the no change threshold value (figure D).

Fig. D

I opened the output images that the models generated in ArcMap and made a map of the changes that were detected (see Results Figure 3).

Results

Results Fig. 1 The area of interest as a subset of the original image from part one.

Results Fig. 2 Comparison of mosaics done by Mosaic Express and Mosiac Pro.

Results Fig. 3

Sources

Satellite images are from Earth Resources Observation and Science

Center, United States Geological Survey. 

 Earth Resources Observation and Science (EROS) Center. U.S. Department of the Interior, U.S. Geological Survey. (2016, April 16). Retrieved May 20, 2016, from http://eros.usgs.gov/ 

Shapefile is from Mastering ArcGIS 6th edition Dataset

by Maribeth Price, McGraw Hill. 2014.