Semi-supervised Interactive Unmixing for Hyperspectral Image Analysis

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. Hyperspectral unmixing is conducted using an unsupervised approach. Fully supervised hyperspectral unmixing approaches are generally infeasible as the number of pixels in a hyperspectral image is often in the tensor hundreds-of-thousands and obtaining accurate pixel-level labels is prohibitively expensive or simply impossible. Thus, many hyperspectral unmixing algorithms in literature only use the hyperspectral image. However, there are often other ancillary information regarding the image available that could be used to assist the unmixing process and do not add an infeasible amount of labeling tasks. Motivated by this, novel semi-supervised hyperspectral unmixing techniques are proposed. The proposed semi-supervised approaches allow supervision over hyperspectral unmixing to improve the unmixing performance but do not require complete pixel-level accurate labels for a training data set. For the