I am hoping to extract some simple vegetation features (sagebrush) from 1m 3-band (RGB) NAIP imagery. Unfortunately, there is no near-infrared band available for this dataset and I need to use this particular imagery for a time-series analysis, so I am stuck with the 3-bands. If this were 4-band imagery, I would consider adding NDVI and EVI vegetation indices as ancillary data for the classification. I do plan on incorporating texture into the classification.
What additional band indices or useful information from widely available data (e.g. NED, landform) can I incorporate into the classification to increase the accuracy? I am flexible in what classification approach that I take.
Best Answer
I did this type of thing for a college project some years back using 25cm aerial photography. It is a difficult thing to accomplish. I ran a number of texture analysis on the imagery and added the bands to the RGB imagery to have more information during the classification process. While it is not a substitute for the NIR band, it did provide some additional information that increased the classification accuracy.