Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity.

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Deep convolutional neural networks (CNNs) have brought in revolutionary achievements in image classification and target detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normalization, dropout, Parametric Rectified Linear Unit (PReLu) activation function. By taking advantage of the specific characteristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Experimental results demonstrate that our proposed CNN outperforms the state-of-the-art methods.

Janus nanoparticles (JNPs) have been widely researched for numerous biomedical applications. However, few studies have integrated enhanced photothermal therapy, effective chemotherapy, and diagnostic imaging in a single nanoplatform. Here, unique Prussian blue@polyacrylic acid/Au aggregate JNPs (PB@PAA/Au-A JNPs) are prepared by a facile and mild method. The exposed surfaces in the JNPs maintain and utilize the original photothermal properties of the PB and Au domains. A heterostructure also adds the functionality of drug loading, which cannot be realized in a common core-shell structure. Notably, the coupling effect of two distinct PB and Au-A domains leads to a superior photothermal conversion efficiency () of 49.4%, which is 9.9% higher than that of PB NPs. Synthesized doxorubicin-loaded PB@PAA/Au-A JNPs (DOX-loaded PB@PAA/Au-A JNPs) can be applied to computed tomography imaging-guided chemotherapy and enhanced photothermal therapy, collectively promoting tumor inhibition.

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Spectral variation is profound in remotely sensed images due to variable imaging conditions. The wide presence of such spectral variation degrades the performance of hyperspectral analysis, such as classification and spectral unmixing. In this letter, 1 -based low-rank matrix approximation is proposed to alleviate spectral variation for hyperspectral image analysis. Specifically, hyperspectral image data are decomposed into a low-rank matrix and a sparse matrix, and it is assumed that intrinsic spectral features are represented by the low-rank matrix and spectral variation is accommodated by the sparse matrix. As a result, the performance of image data analysis can be improved by working on the low-rank matrix. Experiments on benchmark hyperspectral data sets demonstrate the performance of classification, and spectral unmixing can be clearly improved by the proposed approach.Index Terms-Classification, hyperspectral imagery, low-rank matrix approximation, spectral unmixing, spectral variation.