Search Results (123)

Hyperspectral imaging (HSI) is an effective technique for material identification and classification, utilizing spectral signatures with applications in remote sensing, environmental monitoring, and allied disciplines. Despite its potential, the broader adoption of HSI technology is hindered by challenges related to compactness, affordability, and durability, exacerbated by the absence of standardized protocols for developing practical hyperspectral cameras. This study introduces a comprehensive framework for developing a compact, cost-effective, and robust hyperspectral camera, employing commercial off-the-shelf (COTS) components and a volume phase holographic (VPH) grism. The use of COTS components reduces development time and manufacturing costs while maintaining adequate performance, thereby improving accessibility for researchers and engineers. The incorporation of a VPH grism enables an on-axis optical design, enhancing compactness, reducing alignment sensitivity, and improving system robustness. The proposed framework encompasses spectrograph design, including optical simulations and tolerance analysis conducted in ZEMAX OpticStudio, alongside assembly procedures, performance assessment, and hyperspectral image acquisition via a pushbroom scanning approach, all integrated into a structured, step-by-step workflow. The resulting prototype, housed in an aluminum enclosure, operates within the 420–830 nm wavelength range, achieving a spectral resolution of 2 nm across 205 spectral bands. It effectively differentiates vegetation, water, and built structures, resolves atmospheric absorption features, and demonstrates the ability to distinguish materials in low-light conditions, providing a scalable and practical advancement in HSI technology. Full article

The stray light suppression design aims to reduce the impact of stray light on optical systems. For high-resolution optical remote sensing systems, practical tests of stray light suppression performance are essential to ensure optimal functionality. However, due to system complexity and spatial constraints, physical test methods for evaluating the stray light suppression performance of large-aperture, long-focal-length remote sensing cameras remain scarce. To address this issue, a comprehensive test is conducted on the stray light suppression performance of the Jilin-1 GF04A satellite remote sensing camera by integrating multiple test methods, including the environmental light effect test, neighborhood point source response test, key surface response test, and sneak path of stray light test. The experimental results indicate that the stray light response ratios obtained from different test methods are all below 1%. The on-orbit performance of GF04A further validates the effectiveness of its stray light suppression design. Full article

The linear state space model of an optical remote sensing camera with an aperture of φ572 mm was established using the structural dynamics and linear state space theory. Modal reduction was carried out using the balanced reduction method. Combined with the controllable and observability matrix, the model order was reduced. To obtain the frequency response curve between the excitation input point and the response output point, we performed a frequency response analysis with the reduced state space model. The initial frequency response curve was plotted and compared with the response curves of the DC gain method and the balanced reduction method. The accuracy and rationality of the simulation analysis were verified by dynamic tests. The balanced reduction method under state space representation provides a new method for studying the dynamics of lightweight opto-mechanical structures. It can characterize the inherent properties of the system by using the reduction model and has higher computational efficiency, which is helpful to analyze the frequency response characteristics of complex linear systems quickly and accurately. Full article

The Chinese Chang’E-7 (CE-7) mission is planned to land in the lunar south polar region, and then deploy a mini-flying probe to fly into the cold trap to detect the water ice. The selection of a landing site is crucial for ensuring both a safe landing and the successful achievement of its scientific objectives. This study presents a method for landing site selection in the challenging environment of the lunar south pole, utilizing multi-source remote sensing data. First, the likelihood of water ice in all cold traps within 85°S is assessed and prioritized using neutron spectrometer and hyperspectral data, with the most promising cold traps selected for sampling by CE-7’s mini-flying probe. Slope and illumination data are then used to screen feasible landing sites in the south polar region. Feasible landing sites near cold traps are aggregated into larger landing regions. Finally, high-resolution illumination maps, along with optical and radar images, are employed to refine the selection and identify the optimal landing sites. Six potential landing sites around the de Gerlache crater, an unnamed cold trap at (167.10°E, 88.71°S), Faustini crater, and Shackleton crater are proposed. It would be beneficial for CE-7 to prioritize mapping these sites post-launch using its high-resolution optical camera and radar for further detailed landing site investigation and evaluation. Full article

Digital time-delay integration (TDI) enhances the signal-to-noise ratio (SNR) in infrared (IR) imaging, but its effectiveness in push-broom scanning is contingent upon maintaining a stable image shift velocity. Unpredictable image shifts and rotations, caused by carrier or scene movement, can affect the imaging process. This paper proposes an advanced technical approach for infrared multispectral TDI imaging. This methodology concurrently estimates the image shift and rotation between frames by utilizing a high-resolution visible camera aligned parallel to the optical axis of the IR camera. Subsequently, parameter prediction is conducted using the Kalman model, and real-time compensation is achieved by dynamically adjusting the infrared TDI integration unit based on the predicted parameters. Simulation and experimental results demonstrate that the proposed algorithm enhances the BRISQUE score of the TDI images by 21.37%, thereby validating its efficacy in push-scan imaging systems characterized by velocity-height ratios instability and varying camera attitudes. This research constitutes a significant contribution to the advancement of high-precision real-time compensation for image shift and rotation in infrared remote sensing and industrial inspection applications. Full article

We propose a Multispectral Earth Polarization Imager (MEPI), which is located at the Earth–Moon system’s Lagrange point L1. The imager can be used to measure the sunlight reflected by the Earth and the Moon. The measured sunlight has specific polarization information and spectral information, which can provide strong support for a comprehensive understanding of the Earth system and the construction of a perfect Earth–Moon system model. The MEPI provides multispectral images with wavelengths of 400–885 nm, and uses four sub-aperture systems to share a main system. The imager can capture the two-dimensional shape and polarization spectral information of the entire Earth at a spatial resolution of 10 km, and all spectral images can be simultaneously acquired on a single detector. The optical system of the instrument was designed and simulated. The simulation and analysis results showed that the camera can obtain high-quality images of the Earth disc with a 2.5° field of view (FOV). The novel MEPI provides a new way to generate climate-related knowledge from the perspective of global Earth observation. The imager can also be used for lunar observation to obtain spectral polarization information on the lunar surface. In addition, it also shows great potential in other applications of space remote sensing spectral imaging. Full article

Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring. Full article

CubeSats have emerged as a promising alternative to satellite missions for studying remote areas where satellite data are scarce and insufficient, such as coastal and marine environments. However, their standard size and weight limitations make integrating remote sensing optical instruments challenging. This work presents the development of Bentayga-I, a CubeSat designed to validate PANDORA, a self-made, lightweight, cost-effective multispectral camera with interchangeable spectral optical filters, in near-space conditions. Its four selected spectral bands are relevant for ocean studies. Alongside the camera, Bentayga-I integrates a power system for short-time operation capacity; a thermal subsystem to maintain battery function; environmental sensors to monitor the CubeSat’s internal and external conditions; and a communication subsystem to transmit acquired data to a ground station. The first helium balloon launch with B2Space proved that Bentayga-I electronics worked correctly in near-space environments. During this launch, the spectral capabilities of PANDORA alongside the spectrum were validated using a hyperspectral camera. Its scientific applicability was also tested by capturing images of coastal areas. A second launch is planned to further validate the multispectral camera in a real-world scenario. The integration of Bentayga-I and PANDORA presents promising results for future low-cost CubeSats missions. Full article

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R² = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R² = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. Full article

Fourier ptychography (FP) can break through the limitations of existing optical systems with a single aperture and realize large field-of-view (FOV) and high-resolution (HR) imaging simultaneously by aperture synthesis in the frequency domain. The method has potential applications for remote sensing and space-based imaging. However, the aperture stop of the imaging system was generally set to be much smaller than the system with an adjustable diaphragm, so it failed to make full use of the imaging capability of the system. In this paper, a reflective remote FP with full aperture is proposed, and the optical aperture of the camera is set to be the maximum according to the sample-matching condition, which can further improve the imaging resolution by exploring the whole capability of the system. Firstly, the physical model of the remote FP is established using oblique illumination of a convergent spherical wave. Then, the sampling characteristics of the low-resolution (LR) intensity image are analyzed. Assuming diffraction-limited imaging, the size of the aperture of the optical system needs to match the sampling of the detector. An experimental setup with an imaging distance of 2.4 m is built, and a series of LR images is collected by moving the camera for the diffused samples, including the USAF resolution test target and the banknote, where the diameter of the single aperture is set to the maximum to match the size of the CCD pixel under the practical minimum F^# of the camera of 2.8. The high-resolution image is reconstructed by applying the iterative phase retrieval algorithm. The experimental results show that the reconstructed resolution is improved to 2.5×. This verifies that remote FP with full aperture can effectively improve the imaging resolution using only the present single-aperture optical system. Full article

The positional accuracy of satellite imagery is essential for remote sensing cameras. However, vibrations and temperature changes during launch and operation can alter the exterior orientation parameters of remote sensing cameras, significantly reducing image positional accuracy. To address this issue, this article proposes an exterior orientation parameter variation real-time monitoring system (EOPV-RTMS). This system employs lasers to establish a full-link active optical monitoring path, which is free from time and space constraints. By simultaneously receiving star and laser signals with the star tracker, the system monitors changes in the exterior orientation parameters of the remote sensing camera in real time. Based on the in-orbit calibration geometric model, a new theoretical model and process for the calibration of exterior orientation parameters are proposed, and the accuracy and effectiveness of the system design are verified by ground experiments. The results indicate that, under the condition of a centroid extraction error of 0.1 pixel for the star tracker, the EOPV-RTMS achieves a measurement accuracy of up to 0.6″(3σ) for a single image. Displacement variation experiments validate that the measurement error of the system deviates by at most 0.05″ from the theoretical calculation results. The proposed EOPV-RTMS provides a new design solution for improving in-orbit calibration technology and image positional accuracy. Full article

The development of high-resolution and large field of view remote-sensing cameras is inextricably linked to the application of free-form mirrors. The free-form mirror offers higher design of freedom and is more effective at correcting aberrations in optical systems. The surface shape error of a free-form mirror directly affects the imaging quality of remote-sensing cameras. Consequently, a high-precision free-form mirror detection method is of paramount importance. For the convex free-form surface mirror with a large aperture, a hybrid refractive and diffractive testing method combining computer-generated holography (CGH) and spherical mirrors for high-precision null test is proposed in this paper. When comparing the effect of error and the detection sensitivity of different designs, the results showed that the influence of the system error is reduced by about 42% and the sensitivity is increased by more than 2.6 times. The proposed method can achieve higher testing accuracy and represents an effective and feasible approach for the surface shape detection method. Full article

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method. Full article

In recent years, the estimation of tobacco field areas has become a critical component of precision tobacco cultivation. However, traditional satellite remote sensing methods face challenges such as high costs, low accuracy, and susceptibility to noise, making it difficult to meet the demand for high precision. Additionally, optical remote sensing methods perform poorly in regions with complex terrain. Therefore, Unmanned Aerial Vehicle multispectral remote sensing technology has emerged as a viable solution due to its high resolution and rich spectral information. This study employed a DJI Mavic 3M equipped with high-resolution RGB and multispectral cameras to collect tobacco field data covering five bands: RGB, RED, RED EDGE, NIR, and GREEN in Agang Town, Luoping County, Yunnan Province, China. To ensure the accuracy of the experiment, we used 337, 242, and 215 segmented tobacco field images for model training, targeting both RGB channels and seven-channel data. We developed a tobacco field semantic segmentation method based on PP-LiteSeg and deeply customized the model to adapt to the characteristics of multispectral images. The input layer’s channel number was adjusted to multiple channels to fully utilize the information from the multispectral images. The model structure included an encoder, decoder, and SPPM module, which used a multi-layer convolution structure to achieve feature extraction and segmentation of multispectral images. The results indicated that compared to traditional RGB images, multispectral images offered significant advantages in handling edges and complex terrain for semantic segmentation. Specifically, the predicted area using the seven-channel data was 11.43 m² larger than that obtained with RGB channels. Additionally, the seven-channel model achieved a prediction accuracy of 98.84%. This study provides an efficient and feasible solution for estimating tobacco field areas based on multispectral images, offering robust support for modern agricultural management. Full article

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera’s unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras. Full article