The application of neural network algorithms for the quantification of Volatile Organic Compounds (VOCs) concentrations, derived from infrared absorption spectral data, has been shown to achieve superior accuracy compared to conventional least-squares regression techniques. The current neural network models in use generally have issues with low precision and stability, which affect the consistency and credibility of the inversion results. In this study, in order to resolve the aforementioned challenges, we present the pioneering application of the MiniRocket (minimally random convolutional kernel transform) model for the quantitative analysis of VOCs concentrations utilizing hyperspectral data derived from satellite platforms. This model surpasses traditional neural network methodologies by virtue of its automated feature extraction, enhanced computational efficiency, near-deterministic processing, and superior predictive accuracy. The near-deterministic nature of MiniRocket's transformation process ensures reproducibility, as it guarantees identical outcomes given the same input data across diverse computational settings. We employed a training dataset consisting of 120 infrared hyperspectral data with a spectral resolution of 1 cm-1 and a spectral range of 2.8 to 14.3 μm. Additionally, we utilized a validation dataset comprising 80 sets of test data with randomly assigned concentrations. Experimental results indicate that the MiniRocket model achieves a mean error of prediction (MES) of 6.2×10-3 parts per million (ppm) for the estimation of pollutant gas concentrations, with a processing time reduced to 0.02 seconds. These outcomes not only underscore the model's superior predictive accuracy but also highlight its unparalleled computational efficiency when compared to other existing models in the field.
This paper uses traditional algorithms and deep learning algorithms to recover datacube obtained by CASSI and CSIMS in order to verify that CSIMS outperforms CASSI by comparing the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Relative spectral Quadratic Error (RQE) of the reconstructed datacube. The experimental results show that the datacube of CASSI and CSIMS can be both reconstructed by ADMM-TV algorithm which is the most effective among the traditional algorithms. PSNR of the reconstructed datacube of CASSI is 32.50 dB, while that of CSIMS is 35.53 dB, with an increase of 3.03 dB. By using deep learning algorithm, both systems improve substantially under the PnP-HSI network, with PSNR of CASSI growing to 38.85 dB and that of CSIMS growing to 41.97 dB, which can be seen that CSIMS is still 3.12 dB higher than CASSI.
This paper introduces the optical design method of an IMS prototype and proposes an entire optical system optimization approach. The final performance evaluation reveals that the optimized system could meet the requirements. The spectral range of the prototype is designed to be from 450 nm to 700 nm, containing 31 bands. The spectral resolution at the central wavelength is about 8 nm. The field angle (2ω) is 1.86 deg, and the spatial angle resolution (ωΔ) is designed to be 0.013 deg.
The data acquired by the space-borne interference hyperspectral imager is a hybrid interferogram cube. For this kind of complicated data, there are many compression schemes for the on-board compression encoder. How to determine the optimal compression scheme and the optimal compression parameters under this compression scheme is crucial to recovering data. This paper proposes three compression schemes for space-borne interference hyperspectral images, which are mixed interferograms, pure interferograms, and fast views, respectively, and performs a compression evaluation. The peak signal-to-noise ratio of the recovered image, minimum quadratic error, and the spectral angle corresponding to the restored spectral curve are used as the measurement indicators to determine the optimal scheme and the optimal compression parameter configuration which are successfully applied to the development of the spectrometer. This paper establishes a scene-rich remote sensing interference hyperspectral image data source, quantitatively evaluates the impact of different compression ratios under different compression schemes, and changes in remote sensing image displacement, guides instrument design and parameter configuration, and lays a good foundation for the data application of space-borne interference hyperspectral images.
Affected by the sensor itself, illumination, atmosphere, terrain and other factors, even if imaging the same region at the same time, the spectral characteristics of ground objects in different remote sensing images are also very different, and the surface parameters, ground object classification and target recognition results of the inversion are also different, which brings great uncertainty to quantitative analysis. The relative radiation correction effect of PIF, method is obvious and the operation is simple, and the accuracy of the effect depends greatly on the selection of the PIF point. The general relative radiometric correction methods are linearization correction without considering the nonlinear difference of multi-temporal images. At present, most radiation normalization methods assume that the transformation relation between images is linear, extract PIF points and establish radiation transformation model. In this paper, Kernel Canonical Correlation Analysis (KCCA) is used for the first time to normalize the radiation between multi-temporal hyperspectral images, which can greatly reduce the nonlinear difference in relative radiation correction. Based on the theory of nuclear canonical correlation analysis, the radiation normalization method of multi-temporal aerial hyperspectral images is proposed. The feature points of PIF are extracted in the nuclear projection space, and the nonlinear model is used for the radiation normalization of hyperspectral images, to improve the radiation normalization accuracy of multi-temporal hyperspectral images. Compared with Canonical Correlation Analysis (CCA), the number and precision of PIF point extraction can be significantly improved. This method can satisfy the radiation normalization between aerial hyperspectral multi-temporal images.
After years of development, military camouflage has formed a set of theoretical and technical systems represented by color camouflage. At present, a large number of camouflage technology research has been carried out for multispectral reconnaissance of visible and near-infrared. In order to better detect and identify the camouflage target, it is necessary to expand the new reconnaissance band and improve the spectral resolution of the reconnaissance instrument. In this paper, the research on camouflage target recognition technology is carried out through short-wave infrared hyperspectral imaging technology, and the camouflage target is identified by SAM, ACM and CEM algorithms respectively, and the characteristics of three methods in short-wave infrared camouflage target recognition are verified. This research can improve the ability to detect and identify camouflage targets and provide a new means for modern battlefield reconnaissance.
In order to realize the self-test of the camera controller, based on the analysis of the basic functions and test tasks of the Star, GPS and other units, a design scheme of the camera sub-system geophysical controller is proposed, The design and implementation of the bus command, OC switch instruction, auxiliary data release and analog telemetry parameters are described in detail. The preparation of the host computer command and telemetry interface software is carried out in a large number of tests and tested with the controller, and the test results were analyzed and summarized.
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