Structure-Adaptive Convolutional Neural Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification based on deep learning is a hot research topic. The convolutional model employs a single rectangular window to interpret the sample neighborhood features, whereas effective characterization of the complex spatial structure of HSI is still an unsolved problem. In this article, we propose a structure-adaptive convolutional neural network (SACNN) for HSI classification, which efficiently exploits the intrinsic spatial geometry information. Four novel strategies are designed to construct the proposed SACNN network. First, superpixel homogeneous region (SHR) sample generation is introduced to achieve neighborhood features within the intercepted rectangular window of the superpixel. Second, online batch-wise standardization uses zero padding to unify the size of inputs in the same batch, thereby realizing parallel processing of irregular inputs. Third, structure-adaptive convolution (SConv) and structure-adaptive average pooling (SAP) are correspondingly constructed to extract deep spectral, spatial, and geometric features from the effective mapping area of superpixels, and further aggregate the information within irregular boundaries. Finally, a sample-adaptive loss weight (SLW) scheme is designed to adjust the influence of different labels on the same input. Experimental results show that the overall classification accuracy of SACNN reaches 93.11%, 90.96%, and 85.04% for 15 randomly selected training samples per class on three HSI datasets, respectively, obtaining an improvement of 0.97%-2.97% with respect to the best-compared method.