Abstract
Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries–Matusita (J–M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.
Highlights
Numerous studies have demonstrated the ability to exploit the spectral features of minerals and rocks from the reflected solar spectra for lithological mapping and mineral exploration
Spectral-feature-based approaches for lithological identification mainly focus on the conversion and enhancement of spectral features, such as principal component analysis (PCA), spectral angle mapping (SAM), band ratio (BR), relative absorption band depth (RBD), false color composite (FCC), matched-filtering, and combinations of these methods [8,9,10]
Prior studies have demonstrated the capability of integrating textural features with spectral features for image classification and pattern recognition
Summary
Numerous studies have demonstrated the ability to exploit the spectral features of minerals and rocks from the reflected solar spectra for lithological mapping and mineral exploration. Based on the spectral features, from visible to near-infrared (VNIR) wavelengths, some iron-bearing minerals, such as goethite and hematite, have been found in prior research to be relatively easy to distinguish [1,2]. Because of the fundamental vibrations of Al–OH, Mg–OH, and CO3 2− bonds in shortwave infrared (SWIR) wavelengths, many alteration minerals such as carbonates, sulfates, hydroxides, and oxides have been successfully identified and mapped using remote sensing methods [1,2,3,4]. In thermal infrared (TIR) wavelengths, igneous rocks with relatively high SiO2 contents have diagnostic emission spectral features due to the vibration of the Si–O bond, based on which, some rock indices have been constructed, such as the sulfuric acid rock index and the carbonate rock index [5,6,7].
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