SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA USING 3D-2D CONVOLUTIONAL NEURAL NETWORK AND INCEPTION NETWORK

Abstract

Hyperspectral imaging (HSI) classification has recently become a field of interest in the remote sensing (RS) community. However, such data contain multidimensional dynamic features that make it difficult for precise identification. Also, it covers structurally nonlinear affinity within the gathered spectral bands and the related materials. To systematically facilitate the HSI categorization, we propose a spectral-spatial classification of HSI data using a 3D-2D convolutional neural network and inception network to extract and learn the in-depth spectral-spatial feature vectors. We first applied the principal component analysis (PCA) on the entire HSI image to reduce the original space dimensionality. Second, the exploitation of the spatial hyperspectral input features contiguous information by 2-D CNN. Besides, we used 3-D CNN without relying on any preprocessing to extract deep spectral-spatial fused features efficiently. The learned spectral-spatial characteristics are concatenated and fed to the inception network layer for joint spectral-spatial learning. Furthermore, we learned and achieved the correct classification with a softmax regression classifier. Finally, we evaluated our model performance on different training set sizes of two hyperspectral remote sensing data sets (HSRSI), namely Botswana (BT) and Kennedy Space Center (KSC), and compared the experimental results with deep learning-based and state-of-the-art (SOTA) classification methods. The experiment results show that our model provides competitive classification results with state-of-the-art techniques, demonstrating the considerable potential for HSRSI classification.