Abstract

We present an optimal integration of multi-sensor datasets, including Advanced Spaceborne Thermal and Reflection Radiometer (ASTER), Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and digital elevation model for lithological classification using Machine Learning Models (MLMs). Different input features such as spectral, spectral and transformed spectral, spectral and morphological, spectral and textural, and optimum hybrid features were derived and evaluated to accurately classify different rock types found in the Chhatarpur district (Madhya Pradesh), India using the Support Vector Machine (SVM) and Random Forest (RF). The SVM achieves better classification accuracy and shows less sensitivity to the number of samples used in model development. The optimum hybrid features outperform other input features with an overall accuracy and κ coefficient of 77.78% and 0.74, which is around 15% higher as obtained using ASTER spectral data alone. Thus, the proposed multi-sensor optimal integration approach is recommended for successful lithological classification using MLMs.

摘要

我们提出了多传感器数据集的最佳集成，包括先进的星载热反射辐射计 (ASTER)、相控阵型 L 波段合成孔径雷达 (PALSAR)、Sentinel-1 以及使用机器学习模型进行岩性分类的数字高程模型（传销）。使用支持向量机导出和评估不同的输入特征，例如光谱、光谱和变换光谱、光谱和形态、光谱和纹理以及最佳混合特征，以准确分类印度 Chhatarpur 区（中央邦）发现的不同岩石类型(SVM) 和随机森林 (RF)。SVM 实现了更好的分类精度，并且对模型开发中使用的样本数量表现出较低的敏感性。κ系数分别为 77.78% 和 0.74，比单独使用 ASTER 光谱数据获得的高出约 15%。因此，建议使用多传感器优化集成方法使用 MLM 进行成功的岩性分类。