Page 6 - Support Vector Machine News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Salt-induced weathering is a common phenomenon in stone relics, and its traditional artificial evaluation of severity is greatly affected by subjective consciousness and lacks systematic standards. Here, we propose a hyperspectral evaluation method for quantifying salt-induced weathering on sandstone surfaces in laboratory tests. Our novel approach consists of two parts: data acquisition of microscopic observations of sandstone in salt-induced weathering environments, and machine learning technology for a predictive model. We first obtain the microscopic morphology of sandstone surfaces by near-infrared hyperspectral imaging technique. Then, a salt-induced weathering reflectivity index is proposed according to analyses of spectral reflectance variation. Next, a principal components analysis-Kmeans (PCA-Kmeans) algorithm is applied to bridge the gaps between the salt-induced weathering degree and the associated hyperspectral images. Furthermore, machine learning technologies, such as Ra

Kmeans pcaArtificial neural networkRandom forestSupport vector machineK nearest neighborsDazu rock