FCMm is a package for fuzzy clustering water spectra (or called water color).
Given that the most of water color spectra data sets are considered as the high dimensional
set, the advantage of this method is making Fuzzy Cluster Method (FCM) assign the membership (sum as 1) harder.
Then, it ensures that the desired water types are restricted to their belongings (not too soft). It is
possible to cluster the harm algal bloom water type which can not be produced by FCM with
If you want to cluster your own data sets, it provides an improved Fuzzy Cluster Method (FCMm) by optimizing the fuzzifier value (default but not good being 2). See FCM.new.
You can also use the built-in centroids of typical inland waters produced by Bi et al. (2019) and can simply obtain the Chlorophyll-a concentration by blending three algorithms with relatively low bias. See apply_FCM_m and FCM_m_Chla_estimation.
More than ten algorithms are supported for estimating Chla concentration by remote sensing of reflectance. See run_all_Chla_algorithms.
For the sustainable development of algorithm blending, we have also designed a bootstraping assessment method to find the optimal algorithm for each type of waters. See Scoring_system.
It supports the processing of raster or image data, and can use built-in or user-defined centroids. See apply_to_image.
It also includes several data sets about water color spectra and corresponding water quality parameters and a testing image raster (see help documents for details).
Please see NEWS to get changes in each version.
Maintainer: Shun Bi firstname.lastname@example.org [copyright holder]
Yunmei Li email@example.com
Ge Liu firstname.lastname@example.org [contributor]
Bi S, Li Y, Xu J, et al. Optical classification of inland waters based on an improved Fuzzy C-Means method[J]. Optics Express, 2019, 27(24): 34838-34856.
Jackson T, Sathyendranath S, Mélin F. An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications[J]. Remote Sensing of Environment, 2017, 203: 152-161.
Moore T S, Dowell M D, Bradt S, et al. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters[J]. Remote sensing of environment, 2014, 143: 97-111.
Spyrakos E, O'Donnell R, Hunter P D, et al. Optical types of inland and coastal waters[J]. Limnology and Oceanography, 2018, 63(2): 846-870.
Dembele D. Multi-objective optimization for clustering 3-way gene expression data[J]. Advances in Data Analysis and Classification, 2008, 2(3): 211-225.
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