Semi-supervised interactive unmixing for hyperspectral image analysis
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In the past several decades, hyperspectral imaging has drawn a lot of attention in the field of remote sensing. Yet, due to low spatial resolutions of hyperspectral imagers, often the response from more than one surface material can be found in some hyperspectral pixels. These pixels are called mixed pixels. Mixed pixels bring challenges to traditional pixel-level applications, such as identification and detection of ground targets [1, 2]. To address these challenges, hyperspectral unmixing is often an important step during analysis of hyperspectral imagery. Hyperspectral unmixing is the task of decomposing each pixel into a set of pure material signatures (called endmembers) with the corresponding proportions of each material found in each pixel. In this thesis, novel hyperspectral unmixing approaches are proposed that leverage interactive labeling and semi-supervised approaches to improve unmixing results.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.