In this paper, we present a hierarchical feature learning method called Stacked Tensor Subspace Learning (STSL). It can jointly learn spectral and spatial features of hyperspectral images (HSIs) by iteratively abstracting neighboring regions. STSL is able to learn discriminative spectral-spatial features of the input HSI at different scales. In STSL, the joint spectral and spatial features are extracted using Marginal Fisher Analysis (MFA) and Tensor Principal Component Analysis (TPCA). Then Kernel-based Extreme Learning Machine (KELM), a shallow neural network, is embedded in the proposed method to classify image pixels. The important contributions to the success of STSL are exploiting local spatial structure of HSI by using tensor method and designing hierarchical architecture. Extensive experimental results on two challenging HSI data sets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show that the proposed method can produce good classification accuracy with smaller training sets.