I found that it works best to just reference two images to the same point. To make a tie point use the tie point setting and create a tie point between two non ortho-rectified image. This project did not utilize check points but only considered the residual report and visual comparison. The point type can be changed to GCP’s or check points.
4 points must be chosen to generate a model and provide accuracy points. If you load a DEM this location can use an extracted elevation. This assigns the unlocated point a real location.
This entails selecting a feature that is duplicated in both sets of imagery (preferably close to the ground) and assigning the point a number on both imageries. This is not always accurate as roads can change over time or be inaccurately located depending on the scale of your vector file.ģ) Use an already geocoded image to do a comparison GCP selection. There are several ways to do this.ġ) Have ground markings that have been located on the ground and are obvious in the imagery (i.e white x’s)Ģ) Using a vector file such as roads to mark intersections. This method may be more successful between harvest and snow fall. I attempted the delta cue change detection method with ERDAS but was unsuccessful again due to the land cover change associated with farming. Overall results could have been improved through more rigorous selection techniques of the imagery. I believe this is because the imagery was brighter possibly due to a recent rainfall in the 2013 imager (would need to be compared to weather data). The 1990 imagery achieved better classification results with a simpler process. There is some change detection error likely introduced between the two dates due to the method, but AOI’s chosen typically overlapped between both images. To minimize this cropping to specific study areas could be helpful. Success was much better when using a supervised classification although there were still some issues detecting urban from bareland. Issues encountered were non permanent land cover change (farming) that influenced spectral change detection. The growth of many of the major cities in southern Ontario is apparent in the resulting imagery shown above. The change detection was fairly successful using this method. In retrospect a scene between harvest and snow fall would have been more appropriate for this study and may have resulted in better results.Īdditional issues can include rainfall events which can preferentially affect different plots of land depending on soil type. Southern Ontario is a heavily farmed area meaning that the land cover is constantly changing from bare to vegetated, with changing vegetation. If this is the case you can try stretching the imagery using spectral options or you can try subsetting the imagery to remove the areas if possible that are causing the large contrast and darkening of the image (i.e remaining clouds or hazy areas).įor this specific scene radiometric correction was unsuccessful due to extreme variances between the images. When you open your imagery it may appear dark. Tutorials for these steps are available under the tutorial section of the website. Otherwise you may need to import (manage data –> import data) or stack the files (spectral –> layer stack) in ERDAS. img files (i.e ATCOR file created and translated). Assuming that ATCOR worked on both of your images you may be able to just load the.
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