Applying soft computing for remote sensing data composite algorithms

摘要

Remote sensing of the earth's surface using satellite mounted sensor data is one of the most important methods for global environmental monitoring today. However, when using satellite sensor data, clouds in the atmosphere can interfere with the remote sensing, and specific land points may not be correctly monitored on any given day. In order to overcome this problem, a common alternative is to use multiple day composite data. Multiple day composite data use several con secutive days' remote sensing data, and choose the most accu rate data within the temporal dataset for the same land point. This allows the creation of a more complete dataset by patch ing together data which have had no cloud interference during a specified time period in order to create a clearer, more usable dataset. In this article, we propose the application of soft com puting, namely fuzzy logic, in order to select the clearest data in the temporal interval to use for the composite data. Moder ate resolution remote sensing data of areas in Japan were used for the evaluation, and the results were compared with previ ous composite methods.