A Comparison of Covertype Delineations from Automated Image Segmentation of Independent and Merged Irs and Landsat Tm Image-based Data Sets

Existing image segmentation algorithms have recently been ported to the widely used ERDAS Imagine graphical user interface. Within the USDA Forest Service Region 5 Remote Sensing Lab these algorithms have traditionally been applied to Landsat TM data for the purpose of landscape delineation. A less confining image processing environment, combined with the wide availability of finer resolution data sets, has lead to the possibility of multi-scale delineation of various orders of landscape features from diverse spectral categories. A comparison of image segmentation output is made for five meter IRS panchromatic, thirty meter Landsat TM multispectral, and a merged data set. The merging of satellite imagery is commonly used to generate a product that has enhanced complimentary characteristics. The method introduced consists of performing image segmentation on spatially and spectrally merged data sets. The results indicate segment delineations using a merged data set for mid and fine scale landscape mapping efforts as a marked improvement over conventional image segmentation procedures.