Unmixing provides a summary of hyperspectral data and is useful for many image processing tasks. Recently, spectral unmixing has also been introduced to hyperspectral image denoising literature. However, so far, only the spectral information has been utilized for unmixing-based denoising. While most of the endmember extraction methods in the literature rely solely on spectral information, it has been shown that spatial-spectral preprocessing (SSPP) methods can enhance endmember extraction performance by utilizing the assumption that endmembers are more likely to be located in homogenous regions. This letter proposes the use of SSPP prior to spectral unmixing, to guide the endmember extraction process to spatially homogenous regions. The enhanced endmember extraction performance in turn leads to enhanced denoising performance. In addition, the proposed approach also goes one step further and retains the anomalous/scarce endmembers, which may include important endmembers, such as rare minerals, stressed crops, or military targets, and which may be lost due to the included spatial preprocessing (SPP) steps. Discarding such anomalous endmembers in a summary or compression of big data may result in undesired consequences. In short, the proposed approach provides enhanced unmixing-based denoising performance, while also retaining the anomalous endmembers.