In conventional infrared imaging systems, non-uniformity correction typically involves continuously reading correction parameters from double data rate (DDR) memory. For high-frame-rate short-wave infrared imaging systems to achieve real-time non-uniformity correction, it is essential to minimize the reading time of correction parameters. Due to the narrow dynamic range of two-point correction and the large parameter storage required by two-point multi-segment correction, it is difficult to simultaneously achieve good correction performance and short parameter reading time under limited hardware resources. To address the above issues, this paper proposes a real-time non-uniformity correction method suitable for high-frame-rate short-wave infrared images. Based on a field-programmable gate array (FPGA), improvements are made to quadratic polynomial correction through the design of quantization methods for different parameters to enhance storage bit-width utilization; dynamic allocation of bit-widths between parameters to improve correction performance; and ping-pong buffering for DDR reading to avoid the impact of DDR read latency on parameter reading time. The storage size of the improved correction parameters is comparable to that of conventional two-point correction. Experiments were conducted on a hardware system based on the XC7A100T-2FGG484I FPGA. The experimental results show that the average non-uniformity of images after the improved quadratic polynomial correction is 0.4818%, significantly better than 0.5930% after two-point correction and slightly better than 0.4891% after two-point eight-segment correction. Blind pixel compensation was completed simultaneously with the correction. Using a 640 × 512 area array InGaAs short-wave infrared detector, the highest real-time processing frame rate reaches 800 frames per second (FPS).

Visible–near-infrared (VNIR) hyperspectral imaging provides a non-contact approach for cultural heritage examination. This study presents the design and calibration of a compact finite-conjugate VNIR pushbroom hyperspectral camera for close-range mural imaging. Operating over 400–1000 nm at a nominal working distance of 404 mm, the system provides a mean spectral sampling interval of 4.85 nm and an object-space sampling interval of approximately 82.4 μm/pixel. An integrated calibration workflow was established for wavelength assignment, spectral response characterization, geometric correction, radiometric calibration, and scan synchronization. The experimental results yielded a modulation transfer function (MTF) of 0.34 at the effective detector Nyquist frequency, a mean spectral response function full width at half maximum (FWHM) of 6.3 nm, a maximum absolute wavelength residual below 0.90 nm, residual smile and keystone errors below 0.3 pixels, a residual radiometric nonuniformity of 0.71%, and a mean signal-to-noise ratio (SNR) of 339. Measurements of Potala Palace mural samples demonstrate the acquisition of spatially detailed, radiometrically corrected hyperspectral data under close-range conditions.

Conventional protective coatings typically suffer from insufficient mechanical durability and weak interfacial adhesion, making them prone to cracking, wear, and peeling during use, which reduces their protective performance. To address this issue, this study employed an electrodeposition method to fabricate a MoS2-based multifunctional lubricating coating on the surface of 304 L stainless steel. MoS2 nanosheets were synthesized via a hydrothermal method and co-deposited with Ca2+ and myristic acid (MA) to form a MoS2/Ca-MA composite coating. Tribological tests indicate that the coating significantly reduces the coefficient of friction and enhances wear resistance, maintaining stable friction-reducing behavior during prolonged friction. Notably, after 30 days of immersion in water, the coating retained a stable coefficient of friction (0.2001) without significant performance degradation, demonstrating favorable friction stability in aqueous environments. Electrochemical test results show that the coating possesses high electrochemical impedance and exhibits effective barrier properties in a 3.5 wt% NaCl solution, significantly enhancing corrosion resistance. Furthermore, the coating demonstrates superhydrophobic properties (contact angle >150°). Even after exposure to friction and corrosion, the coating maintained a high contact angle, suggesting that its micro/nano-structure was partially preserved, thereby continuing to provide interfacial protection. By constructing a MoS2-based composite structure, this study achieved synergistic optimization of friction reduction, wear resistance, corrosion resistance, and superhydrophobicity, providing an effective strategy for the long-term stable protection of metal surfaces in complex friction-corrosion environments.

The measurement of spectral smile and keystone is crucial for both the performance evaluation and spectral retrieval of hyperspectral imagers. However, traditional methods are often either complex in procedure or low in accuracy. To address this issue, this paper proposes the discrepancy function matching (DFM) method and the error-predicted squared weighted centroiding (EPSWC) method for fast and high-precision testing. Considering the influence of spectral non-uniformity, a spectral peak-based spectral response non-uniformity (SRNU) correction method is proposed for non-uniformity correction in the spectral dimension. Relevant experimental results show that the SRNU correction-based DFM and EPSWC methods maintain very simple measurement procedures while achieving significantly improved measurement accuracy compared with other approaches. On this basis, a positioning distance evaluation function (PDEF)-based method is proposed to solve the degeneracy problem caused by variations in the shape and width of the spectrometer response function. Relevant simulation results demonstrate that the proposed method can maintain high localization accuracy and width measurement accuracy under different response function shapes and widths. The method proposed in this paper is applicable to various types of response functions. The related research will help improve the accuracy of imaging spectrometer alignment and performance testing, and promote the development of hyperspectral observation technology.

Achieving a high frame rate and high dynamic range (HDR) under complex illumination remains a significant challenge for airborne push-broom visible-near-infrared (VNIR) hyperspectral cameras. Problematic scenarios typically include high-contrast scenes, such as ocean whitecaps alongside deep water or concurrently sunlit and shadowed urban surfaces. To address this, a real-time HDR acquisition system based on a dual-gain complementary metal–oxide–semiconductor (CMOS) image sensor is proposed. Specifically, a four-pixel HDR fusion method is developed, utilizing an optical calibration setup to accurately determine the fusion parameters and configure the spectral region of interest (ROI) for reduced data volume. The complete workflow, encompassing spectral–spatial four-pixel binning and piecewise dual-gain fusion, is implemented on a field-programmable gate array (FPGA) using a dual-port RAM-based buffering strategy and a low-latency five-stage pipeline. Experimental results demonstrate a minimal processing latency of 0.0183 ms and a maximum frame rate of 290 frames/s. By extending the output bit depth from 11 to 15 bits, the system achieves a digital dynamic range of the final output of 2.03 × 104:1, representing a 9.58-fold improvement over the original low-gain data. The fused HDR data maintain high linearity and good spectral fidelity, with spectral angle mapper (SAM) values at the 10−3 level. Featuring a compact and low-power design, this system provides a practical engineering solution for efficient airborne VNIR hyperspectral acquisition.

Indium gallium arsenide-based short-wave infrared (SWIR) imaging has long been recognized as an important approach for all-weather low-light imaging. However, after deep cooling suppresses detector dark current, electronics-induced random row stripe noise becomes dominant and appears as vertically rolling artifacts. Unlike static fixed-pattern noise, this amplitude-random, temporally dynamic row stripe noise cannot be effectively suppressed by conventional destriping methods. To address this issue, we analyze its generation mechanism and propose a spatiotemporal frequency-domain filtering method guided by the physical priors of random row stripe noise. The method reorganizes the degraded image sequence into independent row-temporal slices according to the row-wise coupling characteristic of the noise. Subsequently, it detects and isolates the moving targets using structure-tensor motion awareness to avoid motion artifacts during frequency-domain filtering. The motion-isolated slices are transformed to the frequency domain, where an inverted Gaussian notch filter is designed to suppress stripe noise based on its low-spatial-frequency prior. Finally, a 1D purification rule is applied to extract the stripe component and reconstruct the destriped image sequence. Experiments in four low-light SWIR scenes showed that the proposed method removes random row stripe noise more effectively than representative destriping methods while preserving image details. This study can facilitate the subsequent applications of low-light SWIR imaging technology.

CoCrNi medium entropy alloy (MEA) coatings were prepared by high velocity oxy-fuel (HVOF) spraying. The microstructures, wear resistance in a wide temperature range, corrosion resistance in NaCl and NaOH solutions of the MEA coatings were systematically studied by SEM, XRD, friction experiment, and electrochemical corrosion test. The microstructure of HVOF coatings was uniform with FCC single-phase solid solution. As the friction temperature increased from room temperature to 200 ℃, 400 ℃, and 600 ℃, the friction coefficients, wear track depth and width, and wear volume loss of the coatings gradually decreased. And these parameters of CoCrNi coating at 600 ℃ are about 0.50 ± 0.1, (140 ± 8) μm, (2.10 ± 0.1) mm, (0.0234 ± 0.0001) g, respectively. Abrasive wear, fatigue wear and plastic deformation were the main wear mechanisms of CoCrNi coatings during the wear test at room temperature. And oxidation wear became the main wear mechanism of CoCrNi coating during the wear test at 400 ℃ and 600 ℃. Ecorr and Icorr of the CoCrNi coatings in NaCl solution were approximately (-0.217 ± 0.01) mV and (0.26 ± 0.08) μA/cm2, respectively. Ecorr and Icorr of CoCrNi coatings in NaOH solution were approximately (-0.282 ± 0.02) and (0.04 ± 0.01) μA/cm2, respectively. And the corrosion resistance of CoCrNi coatings were more superior than 45 carbon steel.

Significance Spaceborne optical imaging technology serves as a key means for Earth observation and deep space exploration. Among these, the short-wave infrared (SWIR) band, leveraging its unique spectral characteristics, plays an increasingly important role in fields such as resource exploration, environmental monitoring, and moon exploration. To acquire high-quality remote sensing data, the design and development process of spaceborne imaging payloads requires a comprehensive trade-off among various key performance indicators. Achieving higher detection sensitivity in different application scenarios represents one of the core objectives in instrument design and development. With the ongoing advancements in advanced focal plane detector technology, high-precision information acquisition technology, precision opto-mechanical design, and manufacturing technology, as well as efficient cooling technology, the detection sensitivity in the SWIR band is steadily improving, thereby offering greater possibilities for the spaceborne applications of SWIR imaging instruments. Therefore, it is necessary to summarize the typical application scenarios of SWIR, along with the technologies and design approaches employed by representative instruments, to provide references for the design of various future instruments. Progress To fully leverage the spectral characteristics of SWIR, in spaceborne applications, SWIR imaging technology is typically integrated into multispectral or hyperspectral imaging payloads. The primary application directions include wide-swath multispectral applications, typical hyperspectral applications for Earth observation, hyperspectral applications for greenhouse gas monitoring, and deep space exploration applications. Wide-swath spaceborne SWIR imagers can observe tens of thousands of square kilometers of Earth surface area during a single orbital pass, offering high temporal and spatial observation efficiency. Meanwhile, the design incorporating multiple discrete spectral bands enables targeted capture of the spectral characteristics for surface features while balancing data volume and processing complexity. The US Landsat series and the European Space Agency's Sentinel-2 series exhibit strong data continuity and openness, representing the international advanced level in this direction. Hyperspectral remote sensing technology can simultaneously acquire narrow and continuous spectral information of ground objects along with two-dimensional spatial information, achieving "spectrum-image integration". Current mainstream spaceborne hyperspectral payloads concentrate their spectral range in the visible to SWIR bands. To accommodate diverse remote sensing application requirements, these payloads adopt a balanced design between spatial resolution and spectral resolution, with spatial resolution generally ranging from 30 m to 100 m and spectral resolution generally between 5 nm to 20 nm. The US Hyperion and GaoFen-5 advanced hyperspectral imager (AHSI) are the most representative instruments of this category. SWIR hyperspectral instruments for greenhouse gas monitoring feature extremely high spectral resolution, reaching 0.1 nm or below, but with lower spatial resolution, typically on the order of square kilometers, their design places significant emphasis on suppressing stray light in the optical system. For imaging payloads in lunar exploration missions, the greatest challenge is how to maximize detection sensitivity under stringent constraints on mass, volume, and power consumption. The moon mineralogy mapper (M3) and the Chang'E series visible and near-infrared imaging spectrometer (VNIS) represent the advanced levels in lunar remote sensing detection and in-situ detection, respectively. Conclusions and Prospects In the history of human spaceborne endeavors, SWIR imaging technology has played a pivotal role in multiple fields. In the future, with the continuous development and spaceborne application of large-format, high quantum efficiency, and low-noise SWIR focal plane detectors, the detection accuracy and efficiency of SWIR imaging systems will be further enhanced. Simultaneously, advancements in precision opto-mechanical machining technology and space-efficient cooling technology will drive the further miniaturization and versatility of spaceborne SWIR imaging payloads. The overall detection sensitivity in SWIR band is expected to gradually approach that of the visible light band, thereby making greater contributions to human spaceborne undertakings.

The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction. In satellite laser communication systems, the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation. However, there are strong non-uniformity and blind pixels in the short-wave infrared image, which makes the image distorted and leads to the decrease of spot centroid positioning accuracy. Therefore, the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance. A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two, further model-based localization error simulations are performed, and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640x512 planar array InGaAs shortwave infrared detector. The experimental results show that the non-uniformity of the corrected image is reduced from 7% to 0.6%, the blind pixels rejection rate reaches 100%, the frame rate can be up to 2000 Hz, and the spot centroid localization accuracy is as high as 0.1 pixel point, which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.

Three-dimensional (3D) imaging technology enables simultaneously capturing two-dimensional surface features, depth information, and the spatial structure of the target area, offering broad applications in airborne imaging. Airborne area-array whisk-broom cameras are widely used at low-to-medium altitudes, providing high-speed height ratio airborne imaging due to their ability to achieve wide-field imaging through scanning. Currently, most airborne area-array whisk-broom imaging systems employ a vertical downward view, which limits their ability to fully capture the 3D characteristics of the target area. To overcome this limitation, this study proposes a backward-squint area-array wide-field whisk-broom imaging scheme. However, under such whisk-broom scanning conditions, a misalignment exists between the equivalent rotation axis of the image motion compensation mirror after optical path deflection and the roll scanning axis of the camera. To resolve this problem, we propose an accurate calculation model for non-coaxial image motion compensation. We conducted theoretical analysis and simulation experiments to validate the proposed method, achieving a stabilization accuracy better than 0.65 μrad per compensation cycle during a 45° backward squint and a 90° scanning width. Our research advances airborne area-array whisk-broom imaging technology by proposing a novel backward-squint imaging scheme and an innovative non-coaxial image motion compensation model, which significantly enhance in wide-field squint imaging and 3D modeling.

Airborne area-array whisk-broom imaging systems typically adopt constant-speed scanning schemes. For large-inertia scanning systems, constant-speed scanning requires substantial time to complete the reversal motion, reducing the system''s adaptability to high-speed reversal scanning and decreasing scanning efficiency. This study proposes a novel sinusoidal variable-speed roll scanning strategy, which reduces abrupt changes in speed and acceleration, minimizing time loss during reversals. Based on the forward image motion compensation strategy in the pitch direction, we establish a line-of-sight (LOS) position calculation model with vertical flight path correction (VFPC), ensuring that the central LOS of the scanned image remains stable on the same horizontal line, facilitating accurate image stitching in whisk-broom imaging. Through theoretical analysis and simulation experiments, the proposed method improves the scanning efficiency by approximately 18.6% at a 90 degrees whisk-broom imaging angle under the same speed height ratio conditions. The new VFPC method enables wide-field, high-resolution imaging, achieving single-line LOS horizontal stability with an accuracy of better than 0.4 mrad. The research is of great significance to promote the further development of airborne area-array whisk-broom imaging technology toward wider fields of view, higher speed height ratios, and greater scanning efficiency.

Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades. Despite the high sensitivity of push-broom hyperspectral imagers, they experience limited swath and wavelength coverage. In this study, we report the development of a push-broom airborne multimodular imaging spectrometer (AMMIS) that spans ultraviolet (UV), visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) wavelengths. As an integral part of China’s High-Resolution Earth Observation Program, AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads. It has been mounted on aircraft platforms such as Y-5, Y-12, and XZ-60. Since 2016, AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data. This study describes the system design, calibration techniques, performance tests, flight campaigns, and applications of the AMMIS. The system integrates UV, VNIR, SWIR, and TIR modules, which can be operated in combination or individually based on the application requirements. Each module includes three spectrometers, utilizing field-of-view (FOV) stitching technology to achieve a 40° FOV, thereby enhancing operational efficiency. We designed advanced optical systems for all modules, particularly for the TIR module, and employed cryogenic optical technology to maintain optical system stability at 100 K. Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data. The AMMIS features more than 1400 spectral bands, with spectral sampling intervals of 0.1 nm for UV, 2.4 nm for VNIR, 3 nm for SWIR, and 32 nm for TIR. In addition, the instantaneous fields of view (IFoVs) for the four modules were 0.5, 0.25, 0.5, and 1 mrad, respectively, with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode. This study reports on land-cover surveys, pollution gas detection, mineral exploration, coastal water detection, and plant investigations conducted using AMMIS, highlighting its excellent performance. Furthermore, we present three hyperspectral datasets with diverse scene distributions and categories suitable for developing artificial intelligence algorithms. This study paves the way for next-generation airborne and spaceborne hyperspectral payloads and serves as a valuable reference for hyperspectral sensor designers and data users.

In dealing with optical satellite images, accurate and efficient cloud positioning and masking are often the prerequisites for the subsequent tasks. However, the current cloud masking algorithms have difficulty in achieving these two goals at the same time. On the one hand, though many physically based models such as the Fmask algorithms have been proposed and widely applied, the performance of these methods still has some limitations including manually adjusted parameters in selecting the proper index and poor performance in thin cloud detection. This situation is much worse when applying the built-in Fmask in cloud-computing platforms such as the Google Earth Engine (GEE). On the other hand, an increasing number of sophisticated algorithms based on Deep Learning such as Convolutional Neural Networks (CNN) and Transformers have been proposed, they are, in most cases, deployed in local environments and require huge amounts of computational capacity to accomplish the tasks, which is inefficient and cannot be quickly utilized in large-scale and long-term studies, especially shows limitations with the trial of transferring the model into the GEE platforms. To solve the aforementioned dilemmas, the present study proposes a novel method for cloud masking by integrating deep learning and cloud-computing GEE. First, we construct a cloud dataset that is composed of globally selected cloud-contaminated pixels with the Fmask algorithm. Then, we use this cloud dataset to train a lightweight and flexible deep learning model based on LeNet. Last to screen out cloud pixels. Last, the developed model is transferred into the cloud-computing GEE platform and used to conduct cloud masking for each optical satellite image. The results show that in comparison to the conventional Fmask algorithm, the performance of the proposed exists superior in both detecting thick and thin clouds. More importantly, cloud masking can be achieved without bureaucratic procedures such as first downloading images and uploading the cloud masking, which is often required by locally developed deep learning models. By utilizing the proposed method, accurate and fast cloud detection can be achieved on the GEE platform that can be used in subsequent tasks like image compositing. For example, the generated monthly mean composting images show much better performance in visualizing ground objects when compared to those images based on the Fmask method, as the remaining cloud pixels that cannot be detected by the built-in Fmask algorithms can be more accurately examined. Through the usage of the proposed cloud mask methods, the merits of the powerful strength in data fitting and model optimization of deep learning algorithms and high efficiency in dealing with big data of GEE are naturally integrated, which is expected to shed light on cloud masking and other remote sensing modeling tasks.

The high-entropy composite coatings with TaC and B-reinforced FeCoCrNiCu were successfully prepared by laser cladding to investigate their wear, corrosion, and tribo-corrosion behaviors. Different from the fine grain strengthening caused by the direct addition of TaC, the doping of B induced the obvious enrichment of Cr at the grain boundary and Cr2B is precipitated. Thus, the microhardness of the B-doped coating is strengthened to 343.98 HV0.5, which is 2.14 times that of the substrate. The high proportion of less-angle grain boundaries significantly improves the durability of coatings in corrosive environments by reducing grain boundary energy, reducing active sites and optimizing grain boundary grid stability. And, the B-enhanced coating exhibits chemical inertness, making them less likely to react with corrosive substances in the environment. This property helps inhibit corrosion and wear. The corrosion resistance of the coating is greatly improved. In B reinforced coating, the Icorr value (0.17 x 10-6 A/cm2) is 0.08 times that of the substrate. The advanced corrosion-wear resistance (1.33 x 10-6 mm3/Nm) is also obtained by B-enhanced coating, which decreased by 96.8 % compared to substrate. From the three tests including wear, corrosion, and tribocorrosion, the corrosion-wear behavior of B doped coating is dominated by anti-corrosion.

With the rapid development of imager manufacturing technology, mid-wave infrared (MWIR) array scanning images have been widely used to embody abundant thermal radiation geographic information. Due to the limited field of view (FOV) of the MWIR imaging detector, image mosaicking is essential for combining multiple overlapping images into a larger FOV image. However, MWIR images simultaneously suffer from poor image quality and a low signal-to-noise ratio (SNR), presenting significant challenges to existing mosaicking methods, particularly under low-overlap conditions. To overcome these challenges, this study proposes a robust phase congruency (PC) image mosaicking approach for aerial MWIR array scanning images based on image positions derived from Position Orientation System (POS). First, a joint corner-edge PC (CEPC) feature detection strategy is implemented to enhance feature point detection in MWIR images. Subsequently, a fractional average PC localization and orientation histogram (FAPC-LOH) descriptor is developed to generate robust feature descriptors. Additionally, image pairs and matching correspondences within overlapping regions are filtered using the initial image positions to prevent mismatches and ensure the reliability of feature points. Valid feature points are incorporated into the global consistency rectangling alignment model based on topology analysis to obtain the rectangular mosaicking results. Finally, ground control points (GCPs) are used to correct the planar projection error of the mosaicked images. The proposed mosaicking method is rigorously evaluated on six MWIR datasets collected from three cities, encompassing diverse scenarios, flight altitudes, imaging times, and overlap rates. Results demonstrate that our PC-based approach improves the mean number of inliers (MNI) by 5-12 times, increases the rate of successful matching (RSM) by 21.2-46.93% with an average RSM of 99.74%. It also achieves an average alignment root mean square error (RMSE) of 2.11 pixels and an average geometric positioning accuracy of 1.26 m (RMSE) across six datasets. Furthermore, the alignment results outperform those of representative mosaicking algorithms and popular commercial software, achieving superior global and local alignment along with enhanced positioning accuracy.

The increasing complexity and scale of remote sensing datasets, coupled with the challenges of accurately estimating algorithmic time efficiency, often lead to significant resource waste or even failure when using machine learning algorithms in urgent or resource-constrained scenarios. Accurate time efficiency estimation is critical for deploying effective algorithms, yet it remains challenging due to the many factors influencing computational performance. Traditional methods of evaluating time efficiency often neglect the effects of core model parameters and complex data scales in spectral and temporal dimensions. In addition, inference time, an essential factor in real-world applications, is often overlooked. To address these limitations, we propose the Time Efficiency Assessment Framework (TEAF), a novel method for evaluating the time efficiency of machine learning algorithms. Through mathematical reasoning, TEAF models the training and inference time as functions (ψ) of complex data scales and core model parameters. The strong linear correlation between ψ and the actual runtime allows TEAF to accurately predict the time and cost of machine learning tasks with a low computational overhead before algorithm execution. To validate this framework, we derived TEAF formulations for five classical machine learning algorithms and tested them on state-of-the-art hyperspectral image datasets and Sentinel-2 multispectral datasets. The results demonstrated that TEAF could accurately predict both training and inference time for various algorithms, with a strong linear correlation between ψ and actual runtime (R2>0.942). This study offers a practical solution to the challenges posed by the increasing volume and complexity of data in remote sensing image processing. The code is available at https://github.com/SCUT-CCNL/TEAF.

Due to spacecraft's volume and weight constraints, it is challenging to simultaneously obtain large aperture, high resolution, and hyperspectral information in spaceborne remote sensing systems. We propose a novel hyperspectral imaging system that utilizes a shared primary and secondary mirror design and a coaxial five-mirror optical path for multi-channel separation. By integrating Offner convex grating spectroscopy, the system enables hyperspectral detection from the visible to the long-wave infrared spectrum. Design results indicate that with a primary mirror diameter of 1 000 mm at an altitude of 500 km, the spatial resolution in the visible and short-wave bands exceeds 2 m, in the mid-wave band exceeds 3 m, in the long-wave band exceeds 6 m, and the panchromatic resolution is better than 1 m. The system achieves a full field of view of 2.3 degrees, accommodating a swath width of 20 km for detection. To enhance the system's aberration and distorion correction capabilities, high-order aspheric elements are incorporated to create a telecentric optical path,ensuring optimal matching between the telescope and the spectrometer. Furthermore, we propose housing thespectrometer module in a cooling chamber to effectively mitigate the impact of background radiation fromthe optical structure on image quality. The final design demonstrates excellent imaging quality, a simple lay-out, and a compact structure, enabling the simultaneous acquisition of high spectral information across theentire spectrum. This system has broad applications in satellite-based earth observation and imaging

Hyperspectral payloads with high spatial and spectral resolution, combined with a wide field of view, are crucial for tackling the complexity of coastal and estuarine water ecosystems, enabling effective monitoring of water quality and ecological conditions. This study introduces a modular spectrometer design utilizing multiple sub-modules in an extended slit configuration. The system delivers a spectral resolution of 5 nm (400–1000 nm) and 10 nm (1000–2500 nm), a spatial resolution of 20 m, and a swath width of 80 km. Smile and keystone distortions are maintained below 1/5 of a pixel. Using Modran to simulate solar irradiance, the SNR of different targets under typical background conditions is calculated. Compared to conventional designs, the proposed modular approach provides compactness and high fidelity, effectively addressing size and optical aberration challenges. The simulation results confirm the system’s robustness, setting a benchmark for next-generation coast observation missions, particularly in coastal monitoring, underwater exploration, and dynamic environmental change tracking.