Study on Characteristic Spectrum and Multiple Classifier Fusion With Different Particle Size in Marine Sediments

Abstract

Marine sediments record much information of the ecological process, which highly correlated with global and local environmental change. Particle size and its distributions of sediments indicate different ecological functions, thus are the key questions in marine ecology. The analysis method is tedious and laborious, which is not conducive for in-situ monitoring. Here, a spectral analysis was explored using surface sediments sampled in the intertidal zone of Dongdayang village, Qingdao, China. These samples were dried and sieved to pass through the mesh size of 0.3 mm, 0.2 mm, 0.1 mm, and 0.075 mm, respectively. Then, four types of subsamples were collected with the particle size of 0.3-0.2 mm, 0.2-0.1 mm, 0.1-0.075 mm, and < 0.075 mm, respectively. The visible and near infrared reflectance spectra (226-975nm) of these subsamples with different particle size were measured. Results showed that there was a negative correlation between the spectral reflectance and the particle size. And the characteristic spectra for particle size classification were 926-975nm. These particle size were classified by the support vector machine algorithm. The classification accuracy for the calibration set and validation set was 100% and 89.06%, respectively. Furthermore, the fusion classifier was compared with the single classifier. Three spectral bands were selected as the single particle size classifier, that is 226-325nm, 826-925nm and 226-975nm. These three single classifiers were fused by voting method, forming multiple classifier fusion. A fusion classifier was recommended whose validation set had the classification accuracy of 93.75%, better than any single classifier. Multiple classifier fusion is a good tool for searching the characteristic spectra of the chemical and physical parameter in sediments. This method provides a solution for the division of particle size in sediment.

Combining Linear Classifiers Using Probability-Based Potential Functions

“…EoC have been also applied in many industrial fields like: the optimal stacking ensemble for remaining useful life estimation was proposed in [5], classification of cutting tools [6] or in the in-line detection of surface defects on glass substrates of thin-film transistor liquid crystal displays [7]. In addition, EoCs are used in other applications such as: the marine sediments classification [8], the land cover type classification [9] or in medical diagnostics [10]. The EoC classifiers play also an important role in the multi-label classification problems.…”