Comparison of two feature selection methods for the separability analysis of intertidal sediments with spectrometric datasets in the German Wadden Sea |
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| Institution: | 1. The Saban Research Institute, Children''s Hospital of Los Angeles, Los Angeles, CA 90027;2. Department of Pediatric, University of Southern California, Los Angeles, CA 90027;3. Department of Psychiatry, University of Southern California, Los Angeles, CA 90027;4. Department of Psychiatry, Columbia College of Physicians and Surgeons, New York, NY 10032;5. New York State Psychiatric Institute, New York, NY 10032;1. University of São Paulo, Institute of Biomedical Sciences-I, Department of Physiology and Biophysics, São Paulo, Brazil;2. Nuclear and Energy Research Institute (IPEN), Biotechnology Center, São Paulo, Brazil |
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| Abstract: | The spectral features of intertidal sediments are all influenced by the same biophysical properties, such as water, salinity, grain size or vegetation and therefore they are hard to separate by using only multispectral sensors. This could be shown by a previous study of Jung et al. (2015). A more detailed analysis of their characteristic spectral feature has to be carried out to understand the differences and similarities. Spectrometry data (i.e., hyperspectral sensors), for instance, have the opportunity to measure the reflection of the landscape as a continuous spectral pattern for each pixel of an image built from dozen to hundreds of narrow spectral bands. This reveals a high potential to measure unique spectral responses of different ecological conditions (Hennig et al., 2007).In this context, this study uses spectrometric datasets to distinguish between 14 different sediment classes obtained from a study area in the German Wadden Sea. A new feature selection method is proposed (Jeffries-Matusita distance bases feature selection; JMDFS), which uses the Euclidean distance to eliminate the wavelengths with the most similar reflectance values in an iterative process. Subsequent to each iteration, the separation capability is estimated by the Jeffries-Matusita distance (JMD). Two classes can be separated if the JMD is greater than 1.9 and if less than four wavelengths remain, no separation can be assumed. The results of the JMDFS are compared with a state-of-the-art feature selection method called ReliefF. Both methods showed the ability to improve the separation by achieving overall accuracies greater than 82%. The accuracies are 4%–13% better than the results with all wavelengths applied. The number of remaining wavelengths is very diverse and ranges from 14 to 213 of 703. The advantage of JMDFS compared with ReliefF is clearly the processing time. ReliefF needs 30 min for one temporary result. It is necessary to repeat the process several times and to average all temporary results to achieve a final result. In this study 50 iterations were carried out, which makes four days of processing. In contrast, JMDFS needs only 30 min for a final result. |
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| Keywords: | ReliefF Tidal flats Hyperspectral data Feature selection Random forest Intertidal sediments |
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