In recent years, deep learning has dramatically improved the cognitive ability of the network by extracting depth features, and has been successfully applied in the field of feature extraction and classification of hyperspectral images. However, it is facing great difficulties for target detection due to extremely limited available labeled samples that are insufficient to train deep networks. In this paper, a novel target detection framework for deep learning is proposed, denoted as HTD-Net. To overcome the few-training-sample issue, the proposed framework utilizes an improved autoencoder (AE) to generate target signatures, and then finds background samples which differ significantly from target samples based on a linear prediction (LP) strategy. Then, the obtained target and background samples are used to enlarge the training set by generating pixel-pairs, which is viewed as the input of a pre-designed network architecture to learn discriminative similarity. During testing, pixel-pairs of a pixel to be labeled are constructed with both available target samples and background samples. Spectral difference between these pixel-pairs is classified by the well-trained network with results of similarity measurement. The outputs from a two-branch averaged similarity scores are combined to generate the final detection. Experimental results with several real hyperspectral data demonstrate the superiority of the proposed algorithm compared to some traditional target detectors.

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Sensitive skin is described as an unpleasant sensory response to a stimulus that should not cause a sensation. Sensitive skin affects an increasing proportion of the population. Sixty-seven participants who tested positive to lactic acid sting test were recruited and randomized into two groups to observe the clinical efficacy and safety of a new birch juice spray for repairing sensitive skin. One group used test spray A, while the other group used spray B as a control. Both groups were sprayed six times daily for 28 days. Noninvasive testing instruments were used to measure stratum corneum hydration, sebum content, transepidermal water loss rates, skin blood perfusion and current perception threshold before and after using spray. Facial images were captured by VISIA-CR, and the image analysis program (Image‐Pro Plus) was used to analyze these to obtain the redness value of the facial skin. Moreover, lactic acid sting test scores and participants’ self-assessments were also performed at baseline, week 2 and week 4. Both sprays A and B significantly decreased the lactic acid sting test score, transepidermal water loss rates, skin blood perfusion, and redness, while increasing the stratum corneum hydration. Compared to spray B, spray A increased sensory nerve thresholds at 5 Hz and decreased the transepidermal water loss rates, skin blood perfusion, and lactic acid sting test score. Sprays containing birch juice improved cutaneous biophysical properties in participants with sensitive skin.