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A comparison study between windowing and binary partition trees for hyperspectral image information mining

Remote sensors capture large scenes that are conventionally split in smaller patches before being stored and analyzed. Traditionally, this has been done by dividing the scene in rectangular windows. Such windowing methodology could provoke the separation of spectrally homogeneous areas or objects of interest into two or more patches. This is due to the presence of objects of interest in correspondence to windows' borders, or because the fixed size of the windows does not adapt well to the scale of the objects. To alleviate this issue, the windows can be arranged in an overlapping way, incurring in some data redundancy storage. Recently, tree representations have been used as an alternative to windowing in order to structure and store large amounts of remote sensing data. In this work we explore the benefits of using Binary Partition Trees (BPT) instead of windowing to store hyperspectral large scenes. We are particularly interested in storing the information resulting of local spectral unmixing processes running over a large real hyperspectral scene. We show that under similar conditions BPT allows a better storage of the unmixing information in terms of reconstruction error.