Information mining for automatic search in remote sensing image catalogs

Abstract

The Earth Observation database is almost on the scale of Zettabyte (10²¹ Bytes). Produced at a rapid rate, those data also present great diversity, due to the range of sensor types. In such a manner, this kind of data is also classified as Big Data, and present opportunities, such as the possibility to analyze and integrate different data, as well as challenges, mainly regarding storing and processing steps in order to be available to users. The distribution of this database is normally through catalogues, which searching criteria are limited to traditional metadata, as acquisition date, sensor characteristics and geographical localization. Thus, there is a demand for a tool that enables users to search for images based on phenomena in lieu of date or location in a data fusion perspective. In this manner, this work resulted in a Remote Sensing Image Information Mining (ReSIIM) prototype able to make smart searches in big databases based on well-known and basic targets found in Remote Sensing imagery: cloud, cloud shadow, clear land (land area), water, forest, bare soil, built-up and burned area. For that, the aforementioned targets metadata are extract and stored in databases, enabling to refine and boost searches. Besides the spatialization and discussion of the aforementioned targets along Brazil, ReSIIM was assessed according to its accuracy to identify adequate images in databases for Linear Spectral Mixture Model (LSMM) application, which is one of the main methods available to detect burned areas in the Brazilian Amazon. With the adequate search criteria, ReSIIM was able to retrieve all adequate images, indicating its high potential for this and further applications.