Spatial Validation of Spectral Unmixing Results: A Systematic Review

Abstract

The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed.

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

“…The term validation is defined as "the process of assessing, by independent means, the quality of the data products derived from the system outputs" by the Working Group on Calibration and Validation of the Committee on Earth Observing Satellites [174]. "Ground truth" or "reference data", which are provided by independent means, are usually compared with "data products" to assess their "degree of correctness" or accuracy [175].…”

Spectral imaging of grass species in arid ecosystems of Namibia

“…Figure 2 shows the MNF eigenvalue threshold used for each image and the remaining number of MNF bands where spatial coherence values are higher than the threshold. To estimate the abundance of an endmember in an image, several linear, nonlinear, and partial unmixing techniques have been tested in the last decades (Plaza et al, 2011;Quintano et al, 2012;Heylen et al, 2014;Wei and Wang, 2020;Peyghambari and Zhang, 2021;Cavalli, 2023). The advantage of partial unmixing methods such as Mixture Tuned Matched Filtering (MTMF) over other methods is that knowledge of background and other endmembers is not required to match individual endmembers, while the disadvantage is that this method is not suitable for mapping background (Boardman, 1998;Mundt et al, 2007;Boardman and Kruse, 2011).…”

Assessing Many Image Processing Products Retrieved from Sentinel-2 Data to Monitor Shallow Landslides in Agricultural Environments

“…The second procedure starts with the requirement for selecting Sentinel-2 images (green diamond in the upper left corner of the Figure 3) [45]: the selected images must be acquired at the same date, or nearly the same date, that the GE images were acquired (i.e., the reference data, Table 2). Only two Sentinel-2 images are acquired nearly simultaneously with the GE data (21 April 2018 and 24 March 2022).…”