Module 4: Spatial Enhancements, Multispectral Data, and Band Indices

Module 4: Spatial Enhancements, Multispectral Data, and Band Indices

In this weeks lab we learned even more in ERDAS Imagine and how to interpret our data we are given from different websites. First we learned how to take data from Glovis and EarthExplorer on USGS. When looking at websites it is important to know where it is coming from and that it is a credible source. Also, many of the data is already in the source we need it for ERDAS but it is important to make sure they are .img so they can be looked at in ERDAS. Lastly, we need to know what processing they used to make the images and if we need to do anything else such as taking the haze out of the photo. 

The second thing we learned was using spatial enhancements in both ERDAS Imagine and ArcGIS Pro. In ERDAS and ArcGIS Pro you are able to have the image go through different filters, such as low filter and high filter. Both of the filters do important things to the image depending on what you need it to look like in the final version. The low pass filter allows low frequency data, or data that does not change much from pixel to neighboring pixel, to pass through, removing the high frequency data, or data that changes rapidly from pixel to neighboring pixel. The visible result is the image appears blurred or smoothed. High pass filter allows high frequency data to pass through, or data that changes rapidly from pixel to neighboring pixel, suppressing low frequency data, or data that does not change much from pixel to neighboring pixel. High pass filtering can be useful for finding edges, enhancing lines and edges, or sharpening an image. Being able to do these can help with what it is in your image and show it easily in your final version. Lastly, you can sharpen an image to help get the boundaries of rivers and buildings more precise.

Next we learned how to look at the Histogram data to understand what the image statistical data to help us interpret the data. This is important because it helps the GIS analyst represent the data in a manner that everyone can understand. Also, we learned how to look at the spectral characteristics of the different band combinations to show either True Color, False Color IR, and False Natural Color. Being able to know the different band combinations helps because depending on what you are trying to represent you need to change the image. Lastly we learned calculating differences between different spectral bands, an index can be created to further enhance the appearance of certain features. We created a Normalized Differential Vegetation Index (NDVI) to help distinguish clearcut areas. Knowing how to do this can help enhance features within the image.

Lastly, we took all the things we learned from above to make three different images. This was to help us understand what we could do to data and present it to the public. First image is a False Natural Color to help show the water feature but not be too bright like False IR. The second image is a False Color Infrared to distinguish the mountain top compared to the rest of the landscape. The last image is a True Color to show the different areas of the water feature and how they change colors.