Spatial Distribution of Lufeng Dinosaur Valley Outcrops Based on the GF‐5 Hyperspectral OS–SPA–SVM Model

This study presents a novel approach to identifying the spatial distribution of geological outcrops using remote sensing, with the Lufeng Dinosaur Valley in China as the focal area. We integrate GF‐5 hyperspectral imagery with the automated morphological endmember extraction–pure pixel index (AMEE–PPI) algorithm to improve endmember extraction accuracy. Additionally, we apply a suite of spectral transformation techniques—log(1/R), continuum removal (CR), and first‐order derivative (FD)—combined with feature band selection algorithms including the successive projection algorithm (SPA), iteratively retained informative variables (IRIVs), and competitive adaptive reweighted sampling (CARS), effectively reducing data dimensionality and enhancing model performance. For classification, we evaluate three machine learning models: random forest (RF), k‐nearest neighbor (KNN), and support vector machine (SVM). Among them, the SVM model demonstrates superior accuracy and stability. The proposed original spectral–SPA–SVM (OS–SPA–SVM) model achieves high precision in identifying outcrops, offering practical value for geological exploration and assessment. Our findings contribute theoretically and methodologically to the application of hyperspectral remote sensing and machine learning in geological research, providing insights into the unique geological characteristics of the Lufeng Dinosaur Valley.