This PDF file contains the front matter associated with SPIE Proceedings Volume 12962, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Minerals are natural compounds with certain chemical composition, which have stable phase interfaces and crystallization habits, and are of great significance for inversion of diagenetic and metallogenic geochemical characteristics and exploration. The use of remote sensing information to identify mineral types has achieved significant application results in the field of geology and mineral resources. In this paper, CASI-SASI-TASI airborne hyperspectral data and USGS standard spectrum library are used to establish a remote sensing image simulation method based on the combination of statistical model and Gaussian function, and the full spectrum remote sensing image with a spectral range of 425nm~12050nm and a spatial resolution of 2.25m is simulated. The simulated full spectral data were used to identify and extract 8 mineral information of limonite, hornblende, calcite/dolomite, high alumina sericite, medium alumina sericite, low alumina sericite, chlorite/epidote and quartz in Liuyuan area, Gansu Province, compared with the recognition results of airborne hyperspectral data, it was found that the two have strong consistency, this indicates that the simulated full spectrum remote sensing data in this article has strong practicality in identifying typical mineral information, and can provide important reference for the future development of spaceborne full spectrum high-resolution sensors and common key technologies.

Mining heavy metal ore deposits may lead to an increase in heavy metal element content in surrounding soils, which could pose irreversible harm to the ecological environment and human health. Therefore, analyzing and classifying soils from different mining areas is of great significance and can provide reference for soil management and environmental pollution control. Laser-induced breakdown spectroscopy (LIBS) has gradually become a research hotspot in soil detection due to its fast and pre-treatment-free characteristics. However, traditional LIBS technology has problems such as low sensitivity, high noise, and poor repeatability, which affect its accuracy. Therefore, this paper proposes a soil classification method based on Principal Component Analysis (PCA) of LIBS technology coupled with K-Nearest Neighbor algorithm (KNN). This method first conducts data standardization and PCA pre-processing to eliminate redundant information and improve signal-to-noise ratio. Then, autonomous sampling technology is used to design the KNN machine learning algorithm structure to generate continuous analytical networks for training and testing sets. Finally, the results show that the soil classification accuracy of the PCA-KNN machine learning model can reach 97.531%, proving that the combination of LIBS technology and PCA-KNN can achieve rapid and accurate classification of soils from different mining areas. Therefore, this method has the significance of providing new ideas and methods for soil classification in different regions.

This article describes data processing for background removal, peak matching in spectrum analyses for experiments such as high-throughput experiments, which is subtracting background function from original data. We tested algorithms such as polynomial method, Whittaker-smoothing-based method, spline and morphological method, and make comparison among these common-used background removal algorithm. Using variable control for main parameters in each algorithm, and Euclidean norm for measuring the distance between original data and baseline function. We get the conclusion that morphological takes advantage in that its baseline function is nearest to original data. By analyzing theory, factor choosing and effectiveness, it is clear that regional graphic procedure and segment procedure are more effective. So further experience aim is determined.

Atmospheric turbulence is a major challenge in long-range imaging of ground-based telescopes, especially in the surveillance of space targets, whose observation distance is usually more than 100 km. In this case, space targets are extremely small in images, occupying less than 0.12% of the total image area, and suffer from severe blur and distortion. Consequently, the accuracy of object detection by both conventional and deep-learning-based methods is significantly hampered. Therefore, this paper proposes an effective framework for detecting space target through atmospheric turbulence. The framework incorporates a shallow deblurring module, a transformer-based feature extractor, and a small region proposal network. The training data comprises simulated degraded images of space target images against celestial backgrounds, as well as a selection of images from the Dotav2 dataset. Testing results show that the proposed framework outperforms the general framework, achieving a mean Average Precision (mAP) improvement of over 20%.

Hyperspectral images provide significant spatial and spectral information which are widely used in object detection. Two-stage detectors are commonly employed in hyperspectral object detection, where effective region proposals play a crucial role in accurate object localization. However, during non-maximum suppression (NMS) process, the Intersection over Union (IoU) metric based solely on spatial geometric information is inadequate for discriminating between similar proposals. This results in a substantial number of expected proposals with dissimilar characteristics are eliminated. In this paper, we analyze the spectral information in hyperspectral images to distinguish the characteristics of different proposals. Furthermore, this paper proposes the Spectral IoU (SIoU) by introducing spectral signature differences as a new metric. This improves the ability to differentiate between different object instances and increases the recall rate of bounding boxes with high localization confidence in region proposal stage. Moreover, SIoU can be simply integrated into the hyperspectral objection detection frameworks without introducing additional computational complexity. Extensive experiments on the Semi-Supervised Hyperspectral Object Detection Challenge dataset demonstrate the effectiveness of our method.

The extraction of polarization features is the key to the further application of remote sensors. For conventional remote sensing, the evaluation method of polarization characteristics is polarization sensitivity, which needs to be obtained through polarization testing. For polarization remote sensing, it is necessary to obtain the polarization characteristics of the instrument through polarization calibration. Polarization correction requires not only the measurement of the polarization characteristics of the target, but also the calibration of the polarization characteristics of the instrument, and the ultimate goal is to eliminate the polarization response of the instrument. Therefore, it is necessary to carry out research on polarization feature extraction and correction technology. This paper mainly discusses the difficulties and technical approaches of current polarization feature extraction and correction, analyzes the key technologies and related progress, and provides important reference value for further improving the quantitative acquisition of target information.

For a floating display system using a prism or bread type retro-reflector, non-retro-reflected light is the key causes of the deterioration in image resolution. In the present study, a micro aperture array (pinhole array) is used to enhance image resolution of aerial imaging display based on prism and bread retro-reflector. The effects of different micro aperture parameters on the divergence angle and stray light of the retro-reflector are experimentally studied, and the modulation of the point spread function of different retro-reflectors is also explored in detail. The experimental results show that by properly arranging the micro aperture array, the divergence angle of the retro-reflective light can be effectively reduced; Moreover, the full width at half maxima of the point spread function of the retro reflector has been effectively narrowed. Finally, after the modulation of micro aperture array, the imaging resolution can be increased by 119% compared to the original one. The proposed micro array is low cost, easy processing and flexible when it is applied to retro-reflector based aerial imaging system to provide high image quality.

Mugwort floss, valued in traditional Chinese medicine, varies in therapeutic properties and market price based on origin and production year. Traditional identification methods, due to their destructiveness and low accuracy, often confuse mugwort floss with A.stolonifera and cause a testing waste. Hyperspectral Imaging, a non-contact technique, offers potential for rapid identification of such medicinal materials. In this paper, we explore hyperspectral data to differentiate mugwort and A.stolonifera using deep learning and neural networks. Using a massive hyperspectral dataset from mugwort and wormwood from two regions across four years, we analyzed performance using metrics like Accuracy, Specificity, and F1 Score. The self-attention-based Backpropagation Neural Network model showed the most promising results for accurate classification. This approach has potential future applications in various fields using Hyperspectral data

For nuclear security and safeguard, it is necessary to detect and identify nuclear materials. Laser-induced Plasma Spectroscopy (LIPS，also LIPS) has great potential to rapid identify the elemental composition for on-site nuclear inspection and forensic. To demonstrate the application of on-site identification of suspicious materials, a portable LIPS device was set up. The detection sensitivity of the device for uranium is about tens of ppm. Tests were carried out to identify suspicious materials, each sample was identified within seconds. The results show that the device has successfully identified nuclear materials from the samples with disturbances. It is demonstrated that the portable LIPS can identify target elements in nuclear material on site, providing a novel technology for nuclear security.

As an important parameter characterizing the bi-directional reflectance of objects, the Bidirectional Reflectance Distribution Function (BRDF) is one of the key parameters in the on-orbit substitute calibration of satellite remote sensors based on stable ground targets. It is a crucial factor affecting the calibration accuracy. With the development of quantitative remote sensing, hyperspectral BRDF measurement for calibration and spectral analysis of land surface features has become increasingly important. Efficient and high-quality methods for collecting multi-angle reflectance data of land surfaces are currently a research focus. This study uses co-observation of two spectrometer to measure the BRDF characteristics of the pseudo-invariant calibration site in wild environment. Based on the selected results of the pseudo-invariant calibration site in Northwest China by previous research, BRDF for three different types of land surfaces were measured including desert, Gobi, and saline-alkali land. Based on the Ross-li kernel-driven model, a hyperspectral BRDF characteristic data set of the three different surface types is fitted, and the spatial distribution characteristics and hotspot effects of the BRDF directional reflection of the ground targets are analyzed. The results show that different surface types have different directional reflectance values and different hot spot effects. Therefore, a stable target radiation reference library with different radiation brightness levels can be constructed to provide a benchmark model for long-term consistent radiometric calibration of Chinese remote sensing satellites.

In this study, we introduce a novel approach for achieving narrowband filters in hyperspectral imaging spectrometers. By embedding photonic crystals within distributed Bragg reflectors (DBRs), we create resonant structures. Through meticulous simulations, we optimize a four-layer DBR configuration, resulting in spectral channels with a 3 nm average FWHM and exceeding 99% peak transmittance. Our key innovation lies in using photonic crystals to modulate transmission. By introducing TiO2 periodic structures, we control the effective refractive index and there by tune transmission wavelengths. The method covers a 475-625 nm spectral range with exceptional transmittance. We also investigate incident light angle effects, revealing systematic shifts in transmission peak. Our design offers adaptability by adjusting DBR film thickness for defining operational ranges and selecting TiO2 cylinder radii for precise channel manipulation. Our approach simplifies fabrication and holds potential for cost-effective hyperspectral imaging filters.

Spectral imaging technology based on on-chip splitting provides services for aerospace, industrial and consumer electronics applications. Since each application requires a different set and number of spectral bands, the lack of scalable and high-cost customized splitting schemes hinders the wide application of multispectral imaging. Here, we demonstrate a compact, highly freely customizable imaging spectrometer with initial validation for coal and gangue classification and recognition applications. And the results reflect the potential application of this spectral imaging system in coal and gangue classification and identification. A supervised classification method using support vector machines (SVM) was used to recognize coal and gangue, and the evaluation of classification accuracy shows that more than 82% of the pixels can be correctly classified, and this study provides strong support for the visual sensors with complete spectral band combinations to achieve higher accuracy.

Detection and identification of hydrogen isotopes and their oxides is a key point in emission monitoring of nuclear facilities. Therefore, the establishment of an accurate and stable identification system for hydrogen isotopes and their oxides has important application value in the management of nuclear facilities. Raman spectroscopy is a non-contact and non-destructive component analysis method. This method is based on inelastic scattering of photons generated in the interaction between laser and matter, and can generate different characteristic signal peaks according to the structure of molecular bonds. Therefore, different hydrogen isotopes and their oxides can be qualitatively analyzed by Raman characteristic peaks, and a certain degree of quantitative results can be obtained by signal intensity and spectral peak information. Based on the self-built dual-wavelength laser Raman spectroscopy system (532 nm and 785 nm), the vibration spectra of D-O chemical bonds in heavy water (D2O) were detected and compared, which provided data support for further analysis and identification of nuclear facility emissions.

To enhance the spatial resolution capability of transmission-mode NEA GaAs photocathodes, this study employed a white-light interferometer to measure the surface configurations of photocathode components post thermal compression bonding. Precise fitting of the surface configurations was achieved using Zernike polynomials, successfully deriving the Zernike polynomial coefficients. Further, these calculated results were integrated into optical design software for modeling, aiming to elucidate the relationship between the photocathode's transfer function and surface configurations. The findings clearly indicate that the MTF value corresponding to 60lp/mm remains stable within the 0° and 5° field angles. However, as the field angle extends to 20°, there is a significant decline in the MTF value. Among them, the transfer performance of the plano-concave photocathode decreases most prominently, followed by the flat structure, while the plano-convex structure exhibits the least decline. Overall, this research provides invaluable references for the further advancement of photocathode technology.

In a MEMS mirror-based dual interference Fourier transform spectrometer (FTS) with a laser interferometer as the position sensing mechanism, making the two interferometers coaxial is very challenging. To solve this problem, a single interference MEMS FTS based on asynchronous calibration is designed. This single interference FTS uses a dichroic mirror to couple a laser beam and a broadband light beam into the same interferometer. Since the two optical beams share the same optical path, they will experience the same change when the position of any optical component along the optical path is adjusted. In data acquisition, the two interference signals are acquired asynchronously by the same InGaAs photodetector. This asynchronous calibration can effectively eliminate the laser coupling issue. According to the experimental results, compared with the dual interference spectrometer, the proposed spectrometer based on asynchronous calibration can improve the spectral repeatability and make the system simpler and lower power consumption.

In order to meet the needs of counter photoelectric detection and precision guidance weapons in modern air warfare, the spectral characteristics of smoke image jamming unit were tested. Based on the analysis of the factors affecting the smoke interference performance, a theoretical calculation model is established, and a feasible test method is proposed. A certain type of smoke jamming unit was tested. We obtained the smoke transmittance curveof0.4-0.76um,1.3-3um,3-5um,8-14um and MWIR and LWIR image of typical time period, which laid a foundation for the further development of smoke jamming unit with better performance.

Visible light communication has advantages such as high speed, broadband, green, safety, and low cost. Moreover, visible light communication is not subject to electromagnetic interference, so it is useful in a wide range of application scenarios such as aviation, hospitals, and mines. However, due to the limited spectrum and coverage provided by a single LED, multiple LED coverage is adopted in the indoor layout to provide seamless connection, which also brings spectrum interference in overlapping areas. This paper proposes an indoor visible light interference suppression method based on the backward forward markup (BFM) algorithm. This method not only solves the problem of spectral interference, but also improves throughput while ensuring user fairness. The simulation results show that the BFM algorithm has brought significantly improvements in various aspects, with system throughput increased by 75% and fairness factor increased by 0.3.

The coupling efficiency between the backscattering light field received by the telescope and the single-mode fiber is one of the important parameters affecting the performance of the all-fiber water vapor Raman lidar system. In the process of using Raman lidar to detect water vapor, the telescope receives the backscattered light signal of the system, which is focused and coupled into the single-mode fiber through the microscopic objective lens. The mode field diameter of the selected single-mode fiber is only 4μm. The weak offset will lead to a decrease in coupling efficiency. When the no-load or space-borne lidar detects the atmosphere, the fluctuation of the airflow may cause the platform vibration to produce a position offset, which will reduce the efficiency of coupling the water vapor Raman scattering echo signal into the single-mode fiber. Based on the above problems, this paper designs a single-mode fiber automatic coupling system. In the closed-loop mode, the controller uses the piezoelectric effect to control the three-axis motion platform to automatically track the maximum brightness in the shooting spot, and realizes the coupling alignment between the single-mode fiber and the nitrogen Raman scattering echo (386.7nm) and the water vapor Raman scattering echo (407.8nm). The coupling efficiency is 49.7%, and the automatic adjustment accuracy is sub-micron. The influence of axial offset and lateral offset on the coupling efficiency is analyzed at the incident light wavelength of 407 nm. This provides a new solution for the continuous, stable and efficient acquisition of water vapor signals by all-fiber detection water vapor Raman lidar system.

Diamond films have excellent transmittance from ultraviolet to far infrared, as well as excellent resistance to laser damage, mechanics, dust, rain, and other characteristics. Therefore, diamond films are used in aircraft infrared windows and supersonic flight missile hoods. The surface of supersonic aircraft can cause plasma ablation under intense aerodynamic heating. High temperature gas on the surface has strong vibration, dissociation, and ionization, resulting in many defects in the optical windows and protective covers on the outer surface of the aircraft, which may lead to deterioration of optical performance. In this thesis, microwave plasma chemical vapor deposition (MPCVD) method was used to synthesize high-quality diamond films using high-purity gas, while using magnetron sputtering to deposit multicomponent alloy coating as a protective layer to study the high-frequency plasma ablation effect of diamond films. Raman spectroscopy, visible-infrared transmittance spectra and field emission scanning electron microscopy was used to analyze the spectrum of diamond films before and after high-frequency plasma ablation. It was found that the multicomponent alloy coatings have good ablation resistance and high transmittance in the 1~4μm wavelength range, while the carbon and alloying components remain on the film surface. This research contributes to promoting the supersonic flight application of diamond films and provides data reference for the design of aircraft outer surface materials.

Laser long-path absorption measurements using ground-based laser transmitters and a receiver on a geosynchronous satellite can be an effective method for observing of atmospheric trace gases (such as carbon dioxide and methane) that complement passive satellite sensors and space-borne integrated path differential absorption (IPDA) lidars. It can be also useful for calibration and validation of passive satellite sensors. The authors conducted experiments using a retroreflector on a polar orbit satellite in 1996. The absorption spectrum of ozone in the 9-μm region was successfully measured using TEA CO₂ lasers. However, the optical efficiency of the system and the efficiency of operation were very low, and it was not practical as an operational observation system. One way long-path absorption method to a receiver on a geosynchronous satellite is highly efficient. It does not require a large aperture high-speed satellite-tracking telescope that was required in the retroreflector experiment. Also, the laser power required for the measurement is much lower. In the method using a geosynchronous satellite, the laser transmitters similar to pulsed IPDA lidars can be used for the ground stations. Multiplexing of the measurements from multiple ground stations is possible using time sharing. It would be also possible to apply dual comb spectroscopy, though multiplexing of the measurements is not possible. In this paper, we review the studies on earth-to-satellite laser long-path absorption methods and discuss the feasibility of the method using a geosynchronous satellite.

Polarization lidar plays an important role in detecting the microphysical properties of non spherical aerosol particles in the atmosphere and the indirect interaction between aerosols and clouds. However, there are various complex optoelectronic components in polarization lidar systems, and the imperfect nature of these components often poses difficulties for the system to accurately receive data. To ensure the accuracy of the detection data of a multi wavelength polarization lidar system, precise calibration of the system is a crucial step. This article is based on a three wavelength (355nm, 532nm, 1064nm) polarization lidar system. Based on the ± 45 ° polarization lidar system calibration method proposed by Freudenthaler et al, an improved calibration method is designed to calibrate different wavelength detection channels. The new calibration method corrects some issues with the ± 45 ° method and proposes a calculation method to reduce the impact of cascading multiple dichroic mirrors on the received Signal The simulation results show that the use of the average polarization error angle successfully reduces the impact of cascading multiple dichroic mirrors on the signal. At the end of the article, we validated the error analysis of the proposed method under different detection wavelengths through simulation calculations. The results show that when detecting extremely small atmospheric particles such as atmospheric molecules, the detection error is around 6%. When detecting non spherical particles with a depolarization ratio greater than 0.1, the error of each detection channel can be reduced to less than 3%.

Considering the potential application demands of atmospheric polarization effects in fields such as navigation, remote sensing, and astronomical observations, experimental research has been conducted to investigate the variations of sky polarization degree under different scattering angles and wavelength conditions. The ideal analysis models of atmospheric polarization characteristics have been established based on the Rayleigh scattering theory. The distribution features of polarization degree and polarization angle in the sky were investigated, and their varying laws with the scattering angle were provided by the simulation study. An experimental system for measuring the atmospheric polarization characteristics was constructed using a turntable, division of focal plane (DoFP) polarization camera, telephoto lens, and bandpass filters. The atmospheric polarization patterns were measured and studied at different observing angles and wavelengths. The experimental results indicate that the magnitude of sky polarization degree is closely related to the scattering angle and it reaches its maximum near 90°. At the four experimental measurement wavelengths of 500 nm, 700 nm, 870 nm, and 1065 nm, the measured values of the sky polarization degree were 0.75, 0.64, 0.41, and 0.28, respectively. The sky polarization degree exhibits a downward trend with the increase of wavelength, and the atmospheric polarization effect gradually weakens with the increase of wavelength. These conclusions provide a meaningful reference for selecting appropriate observation angles and wavelength ranges in astronomical observation and remote sensing applications.