Hyperspectral imaging for the spectral measurement of far-field beam divergence angle and beam uniformity of a supercontinuum laser

A Two-Staged Feature Extraction Method Based on Total Variation for Hyperspectral Images

Effective feature extraction (FE) has always been the focus of hyperspectral images (HSIs). For aerial remote-sensing HSIs processing and its land cover classification, in this article, an efficient two-staged hyperspectral FE method based on total variation (TV) is proposed. In the first stage, the average fusion method was used to reduce the spectral dimension. Then, the anisotropic TV model with different regularization parameters was utilized to obtain featured blocks of different smoothness, each containing multi-scale structure information, and we stacked them as the next stage’s input. In the second stage, equipped with singular value transformation to reduce the dimension again, we followed an isotropic TV model based on split Bregman algorithm for further detail smoothing. Finally, the feature-extracted block was fed to the support vector machine for classification experiments. The results, with three hyperspectral datasets, demonstrate that our proposed method can competitively outperform state-of-the-art methods in terms of its classification accuracy and computing time. Also, our proposed method delivers robustness and stability by comprehensive parameter analysis.

Deep-Learning-Based Active Hyperspectral Imaging Classification Method Illuminated by the Supercontinuum Laser

Hyperspectral imaging (HSI) technology is able to provide fine spectral and spatial information of objects. It has the ability to discriminate materials and thereby has been used in a wide range of areas. However, traditional HSI strongly depends on the sunlight and hence is restricted to daytime. In this paper, a visible/near-infrared active HSI classification method illuminated by a visible/near-infrared supercontinuum laser is developed for spectra detection and objects imaging in the dark. Besides, a deep-learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active HSI classification. By applying the method to a selection of objects in the dark successfully, we demonstrate that with the active HSI classification method, it is possible to detect objects of interest in practical applications. Correct active HSI classification of different objects further supports the viability of the method for camouflage detection, biomedical alteration detection, cave painting mapping and so on.