It is a highly important and challenging task to finish the high-accuracy hyperspectral image classification using fewer training samples. A novel hyperspectral image-based classification method (hereafter referred to as the LBP-SSKNN) was proposed by combing Local Binary Patterns (LBP) and K-Nearest Neighbors (KNN). First, the Principal Component Analysis (PCA) was used to reduce the dimension of hyperspectral image. Subsequently, the LBP was used to extract the spatial texture information and the spatial and spectral features were uniformly scaled to form the spatial-spectral vectors. Finally, the vectors were input into the KNN classifier to obtain the classification result. The training and test datasets of three popular open hyperspectral datasets were used to validate the proposed method, including Pavia University, Indian Pines and Salinas. the classification method was verified on three groups of hyperspectral remote sensing image datasets. In addition, three classic classifiers were also selected to compare the LBP-SSKNN, including Radial Basis Function Support Vector Machine (RBF-SVM) and Kernel Simultaneous Orthogonal Matching Pursuit (KSOMP). In the Pavia University dataset and Indian Pines dataset, the 10% of training dataset were randomly selected as the training samples. The overall accuracy (OA) and Kappa coefficient reach 99.15%, 98.87% and 97.88%, 97.58%, respectively. In the Salinas dataset, only the 2% of training dataset were randomly selected as the training samples, and the OA and Kappa coefficient reach 98.46% and 98.29%. The experimental results show that the OA of LBP-SSKNN method can still reach more than 98% under the 10% and even 2% of the training dataset. Our proposed method can satisfy the high-accuracy requirement due to limited training samples in practical application scenes.