A new method of data transformation for satellite images: I. Methodology and transformation equations for TM images

Abstract

These two papers deal with a new method of data transformation. By analysing grey level curves (broken lines) of various ground features in image bands of different satellites, we have found that, inherent in 3‐ or 4‐band satellite images (SPOT, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS) there are three basic remote‐sensing characteristics as follows: (1) the general radiance level L; (2) the visible–infrared radiation balance B; and (3) the band radiance variation vector (direction and speed) V. However, inherent in 5‐ or 7‐band satellite images (NOAA, TM), besides the above three, there is an extra basic remote‐sensing characteristic, i.e. the thermal radiation intensity I. This is denoted by thermal bands, i.e. the TM band 6 or NOAA (AVHRR) bands 4 and 5, which are relatively independent and can be used directly, and hence are unnecessary for information extraction or data transformation. Therefore, the data transformation only lies in extracting the L, B and V from original satellite images. Furthermore, we have also found that there are three basic ground‐cover elements on the Earth's surface: i.e. the bare land (in a broad sense), the vegetation and the water body, which, in different proportions, constitute all ground cover. Moreover, there are three basic (primitive) colours on colour image (including colour composite of satellite images): i.e. red, green and blue, which generate all colours on the colour image. Further research has revealed that the three basic remote‐sensing characteristics, the three basic ground‐cover elements and the three basic colours on the composite can conceptually constitute a three‐to‐three corresponding regular triangle scheme. Perhaps a good method of data transformation should make the scheme realistic, i.e. make the three ‘threes’ all correspond to each other. The research presented here has completed this task by regression calculations and selection of specific variables. First, the methodology and transformation equations for TM images are discussed. The transformed L, B and V images have relatively independent and equally distributed information as well as clear and definite physical, mathematical and geographical significance. They can be used effectively for generating high‐quality colour composites, on which the red, green, blue, yellow, pink, cyan and other colours of various kinds are all generated and all pure, saturated, equilibrated, meaning‐definite and close to the colours of ground features in nature. As a result, interpretations and discriminations of ground features can be easier and conducted not only by experience, but also by logic. The L, B and V images can also be used effectively for classification and digital analysis of ground features. With regard to the transformation equations for SPOT, NOAA, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS images and the method application will be dealt with in the second paper.