###### Abstract  
Recently, deep learning tools have made significant progress in hyperspectral image (HSI) classification. Most of existing methods implement a patch-based classification manner which may cause training test information leakage or waste labeled information for non-central pixels of image patches. Therefore, it is challenging to achieve remarkable classification performance via the traditional convolutional neural networks (CNN) in the absence of label information. Moreover, due to the limitation of convolutional kernel sizes and convolution operations, the spectral information of HSI cannot be fully utilized with a traditional CNN framework. In this paper, we implement pixel-based classification by a special data division strategy and propose a novel spatial-spectral dimensional transformation (SSDT) to obtain spectral features containing more spectral information. Then, we construct a fully convolutional network (FCN) with two branches based on 3D-FCN and 2D-FCN to achieve broader spatial and spectral information interaction. Finally, the fused features are utilized to realize accurate pixel-based classification. We verify our proposed method on three classic publicly available datasets; the overall classification accuracy and average accuracy reach 82.27%/87.85%, 83.81%/81.55%, and 85.97%/83.89%. Compared with the latest proposed method SS3 FCN in the no-information-leakage scenario, the overall classification accuracy of our proposed method is improved by 1.72%, 4.95% and 0.2%, and the average accuracy is improved by 0.95%, 3.92% and 2.67% on the three databases, respectively. Experimental results demonstrate the effectiveness of the proposed SSDT and the proposed CNN framework. hyperspectral images classification; feature extraction; spatial-spectral dimension transformation; semantic segmentation +
Footnote †: journal: Applied Sciences  
However, the classification accuracy of those networks was unsatisfactory when the input data was a one-dimensional vector. Then, the 1D-CNN was employed to extract the viable classification feature from the redundant spectral information. But using only one-dimensional spectral information limits the effectiveness of features such as spectral noise and other factors causing different objects to have the same spectrum or the same object to have different spectra. To solve this problem, 2D and 3D-CNNs [10] were applied to extract the spatial feature or the spectral-spatial feature simultaneously. And recently, the attention mechanism [11; 12; 13] was embedded into the classification model to select the beneficial features from redundant spectral information. Although deep-learning-based HSI classification methods have achieved significant improvements, there are still two challenges. Firstly, the existing HSI classification methods are generally based on central pixel classification [14]. The spatial and spectral information of all pixels in the block is assigned to the central pixel, ignoring the spatial disparity and limiting the receptive field. And only a mere portion of the limited labeled pixels are utilized in the case without training test information leakage. Therefore, we turn our attention to semantic segmentation [15], which can classify each pixel of HSI. This approach not only improves the utilization of label information, but also greatly enhances the efficiency of prediction map generation. Secondly, affected by the sliding convolution mechanism of CNN and the limited size of the convolutional kernel, most of the existing CNN-based methods can only extract the adjacent spectral information and they cannot utilize the correlation information between multiple bands. However, it has been proven that the beneficial information for classification is not always continuous [1; 16]. More extensive spectral information interaction between multiple bands is needed for HSI classification. In this paper, we propose a pixel-based HSI classification method to improve the utilization of label information while achieving a more robust classification.