With success of convolutional neural networks (CNNs) in computer vision, the CNN has attracted great attention in hyperspectral classification. Many deep learning-based algorithms have been focused on deep feature extraction for classification improvement. In this letter, a novel deep learning framework for hyperspectral classification based on a fully CNN is proposed. Through convolution, deconvolution, and pooling layers, the deep features of hyperspectral data are enhanced. After feature enhancement, the optimized extreme learning machine (ELM) is utilized for classification. The proposed framework outperforms the existing CNN and other traditional classification algorithms by including deconvolution layers and an optimized ELM. Experimental results demonstrate that it can achieve outstanding hyperspectral classification performance.

This article presents an intrafusion network (IFN) for hyperspectral image (HSI) super-resolution (SR). Given that the HSI is a 3-D data cube with both the spatial information and the spectral information, the key challenge to construct HSI SR is how to efficiently exploit the spectral information among consecutive low-resolution (LR) bands, besides the spatial information. The proposed IFN consists of three modules, including the spectral difference module, the parallel convolution module, and the intrafusion module, which directly utilizes both the spatial information and the spectral information for reconstructing the high-resolution HSI. Different from most of the existed methods that tackle the spatial and spectral information separately, the proposed spatial-spectral utilization is achieved in one integrated network, which opens up a new way for HSI SR. Meanwhile, applications of this three modules strategy (first spectral difference, then parallel convolution, and finally, intrafusion) on both the conventional convolutional neural network and the residual network with deeper depth have shown the generalization capacity of this proposal. Experimental results and data analysis demonstrate the effectiveness of the proposed method using three hyperspectral data sets.

A hyperspectral image (HSI) super-resolution (SR) is a highly attractive topic in computer vision. However, most existed methods require an auxiliary high-resolution (HR) image with respect to the input low-resolution (LR) HSI. This limits the practicability of these HSI SR methods. Moreover, these methods often destroy the important spectral information. This letter presents a deep spectral difference convolutional neural network (SDCNN) with the combination of a spatial-error-correction (SEC) model for HSI SR. This method allows for full exploration of the spectral and spatial correlations, which achieves a good spatial information enhancement and spectral information preservation. In the proposed method, the key band is automatically selected and super-resolved with the boundary bands. Meanwhile, spectral difference mapping between the LR and HR HSIs can be learned by the SDCNN, and then be transformed according to the SEC model, which aims at correcting the spatial error while preserving the spectral information. The rest nonkey bands will be super-resolved under the guidance of the transformed spectral difference. Experimental results on synthesized and real-scenario HSIs suggest that the proposed method: (1) achieves comparable performance without requiring any auxiliary images of the same scene and (2) requires less computation time than the state-of-the-art methods.

Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method.

The transmission of virtual reality (VR) videos requires huge bandwidth, which brings great challenges for the system to implement real-time applications. This letter proposes a scalable full-panorama video coding method to adapt the insufficient bandwidth, in which user's movement information is utilized as a feedback from the VR device to the video encoder. The regions that the user is interested in are first mapped and then coded in high quality, while the others in low quality. This different-quality coding is achieved through scalable high efficiency video coding (SHVC), which only makes limited modifications on the SHVC encoder and no modification on the SHVC decoder. In addition, full-panorama coding also avoids the reference problem in inter prediction and relieves the delay-sensitive problem. Experiments results show that our proposed method reduces approximately 87% bit rate with no significant decrease in quality of the region of interest in panorama.