High resolution satellite images, such as Landsat TM or SPOT HRV, allow us to extract detailed land cover information. However, if we enlarge those images too much, each pixel appear like a tile and disturbe image interpretation. In this paper, authors have examined the effective enlargement scale of digital satellite images for extracting detailed land cover information by image interpretation. The effective scale can be defined mainly from the human eye resolution and the sensor IFOV. The authors derived a equation which suggests a appropriate scale for patiqular IFOV image. For example, if the IFOV of a satellite image is lOm, we recommend 1/50, 000 scale for enlargement. If the image is enlarged more than this scale, the image quality suddenly decreases. Authors also examined the effect of image interpolation for image enlargement. When one want to enlarge a satellite image more than the above scale, interporation such as Bi-linear or Nearest Neighbor methods are useful for recovering the image quality reduction due to the image enlargement.
Logarithmic Residual (LR), Least Upper Bound Residual (LUB) or Gray Scale Log Residual (GSLR) methods have been used for the purpose of converting digital numbers (DNs) of multiband remote sensing data to apparent band-to-band reflectance patterns. However, converted values by these methods show the specific distribution pattern in theN-dimension space defined by the N-band data, because these methods reduce the dimension toN-1. Thus, application of the spectral indices, which was previously developed for the original DN ofN-dimension, to the converted values may lead incorrect index values. The authors propose Spectral Pattern Index (SPI), which allows us to measure a band-to-band pattern similarity (“spectral pattern” and “spectral contrast”) convertedN-1 dimension values by the LR, LUB and GSLR methods. The Landsat TM data of Teshionakagawa area in northern Hokkaido, Japan, was processed by this new index after conversion to apparent reflectance pattern using the GSLR method. The result show more detailed information on vegetation and lithology.