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The coherent-dispersion spectrometer (CODES) is a new exoplanet detection instrument using the radial velocity (RV) method. This attempts mainly to improve environmental sensitivity and energy utilization by using an asymmetric, common-path Sagnac interferometer instead of a traditional Michelson interferometer. In order to verify its feasibility and to choose the appropriate key parameters to obtain the optimal performance, research on data processing for the design stage of the CODES is performed by systematic simulation and analysis. First, the instrument modelling is carried out for further data analysis according to the principle of the CODES, and the reliability of the model is verified by experiments. Second, the influence of key parameters on fringe visibility is analysed systematically, which provides a certain reference for the choice of the key parameters. Third, the RV inversion method for the CODES is proposed and optimized according to the related analysis results so as to promote RV inversion precision. Finally, the recommended values for the key parameters of the CODES are given. The experimental results show that the data processing error of RV inversion is less than 0.6 m s–1 within the recommended range of key parameters. This indicates that the scheme of the CODES is reasonable and feasible, and that the proposed data processing method is effective and well matched with the instrument design.

Object-based hyperspectral image classification (OBHIC) converts the basic unit from ‘pixel’ to ‘object’ by image segmentation, in order to take advantage of the spatial distribution law of geographical substances, as well as increase classification performances. However, it involves the problem of scale selection, i.e. the segmentation parameters are set manually by empirical values. In this paper, a novel OBHIC algorithm based on adaptive segmentation is proposed. Here, hyperspectral images (HSIs) are first segmented through a new segmentation scheme with constraint ability, and the thresholds for segmentation are calculated adaptively by utilizing training samples. And then K-nearest neighbor algorithm (KNN) is applied to classify the centers of each region after segmentation. In addition, based on the semisupervised idea, semi-known samples are obtained to further improve the classification performance. Experimental results are presented on two HSI datasets. For different HSIs, the adaptive thresholds calculated are consistent with empirical ones, and the developed classification algorithm has achieved good classification results, thus demonstrating strong robustness of the algorithm. For the HSI Indian Pines from AVIRIS sensor, the Overall Accuracy (OA) and kappa are 95.13% and 0.9444 respectively with 10% training samples, and for the HSI Pavia University from ROSIS sensor, the OA and kappa are 95.52% and 0.9416 respectively with 2% training samples. And good classification performance is still maintained for small number of training samples.

Hyperspectral anomaly detection has garnered much research in recent years due to the excellent detection ability of hyperspectral remote sensing in agriculture, forestry, geological surveys, environmental monitoring, and battlefield target detection. The traditional anomaly detection method ignores the non-linearity and complexity of the hyperspectral image (HSI), while making use of the effectiveness of spatial information rarely. Besides, the anomalous pixels and the background are mixed, which causes a higher false alarm rate in the detection result. In this paper, a hyperspectral deep net-based anomaly detector using weight adjustment strategy (WAHyperDNet) is proposed to circumvent the above issues. We leverage three-dimensional convolution instead of the two-dimensional convolution to get a better way of handling high-dimensional data. In this study, the determinative spectrum–spatial features are extracted across the correlation between HSI pixels. Moreover, feature weights in the method are automatically generated based on absolute distance and the spectral similarity angle to describe the differences between the background pixels and the pixels to be tested. Experimental results on five public datasets show that the proposed approach outperforms the state-of-the-art baselines in both effectiveness and efficiency.

Spectral analysis is a practical analytical technique suitable for the study of painting. Especially hyperspectral imaging has gradually become a hot research topic in the scientific analysis of painting relics, due to its characteristics such as non-invasive, non-contact, fast imaging and high spectral resolution. It is mainly used to extract the intrinsic information that is difficult to observe in painting with naked eyes. In this study, two seemingly identical sketch paintings served as samples, one was painted with a 2B pencil, the other with a 4B pencil. A ground-based hyperspectral imager was used to obtain the hyperspectral data cube for the two sketch paintings in the range of 390 nm–1025 nm. And the two paintings were analyzed and compared in terms of spectral characteristics. Results indicated that hyperspectral imaging greatly facilitates the acquisition of the diagnostic spectral characteristics of different materials in painting. And the specific target in paintings can be effectively identified through hyperspectral analysis, which is of great help to painting identification. At the same time, DC characteristics and detail characteristics of the painting can be extracted, which are helpful for the researchers to analyze the painting style. In brief, it is further proved that the hyperspectral imaging has significant scientific and practical value in the protection and preservation of painting relics.

In a traditional dual optics routes Michelson interferometer, the critical time delay is acquired by scanning one mirror that needs a precision physical translation mechanism. The so-called translation mechanism usually dominates the risk, cost, power consumption, and performance of such instruments which limits the development of interference spectrometer. This paper describes the principle of a novel temporal modulated interference spectrometer that generates time delay by using birefringence crystal because of speed difference and the refraction indexes between the ordinary and the extraordinary component. A greater optical path difference can be abstained by cascading a series of wave plates that interlaced with a series of achromatic ferroelectric liquid crystal switches. Through the simulation analysis and experiment of the principle of polarization interference spectrometer, it demonstrates the validity of the instrument principle and data inversion method.