HypeRvieW: an open source desktop application for hyperspectral remote-sensing data processing
作者： Alberto S. Garea , Álvaro Ordóñez , Dora B. Heras , Francisco Argüello
DOI: 10.1080/01431161.2016.1244363
关键词: Preprocessor 、 Modular design 、 Machine learning 、 Data processing 、 Artificial intelligence 、 Data mining 、 Computer science 、 Interface (Java) 、 Hyperspectral imaging 、 Reference data 、 Segmentation 、 Graphical user interface
摘要: In this article, we present a desktop application for the analysis, reference data generation, registration, and supervised spatial-spectral classification of hyperspectral remote-sensing images through simple intuitive interface. Regarding ability, different schemes are implemented by using chain structure as base. It consists five configurable stages that must be executed in fixed order: preprocessing, spatial processing, pixel-wise classification, combination, post-processing. The modular implementation makes its extension easy adding new algorithms each stage or chains. tool has been designed platform is open to incorporation users interested comparing schemes. As an example use, scheme based on Quick Shift QS algorithm segmentation Extreme Learning Machines ELMs Support Vector SVMs also proposed. license-free, runs Linux operating system, was developed C language GTK library, well other free libraries build graphical user interfaces GUIs.
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