Pushbroom Research Articles

Lunar Calibration for ASTER VNIR and TIR with Observations of the Moon in 2003 and 2017

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is a multiband pushbroom sensor suite onboard Terra, has successfully provided valuable multiband images for approximately 20 years since Terra’s launch in 1999. Since the launch, sensitivity degradations in ASTER’s visible and near infrared (VNIR) and thermal infrared (TIR) bands have been monitored and corrected with various calibration methods. However, a unignorable discrepancy between different calibration methods has been confirmed for the VNIR bands that should be assessed with another reliable calibration method. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting a radiometric calibration (called as lunar calibration), which can measure the temporal variation in the sensor sensitivity of the VNIR bands enough accurately (better than 1%). From the lunar calibration, 3–6% sensitivity degradations were confirmed in the VNIR bands from 2003 to 2017. Since the measured degradations from the other methods showed different trends from the lunar calibration, the lunar calibration suggests a further improvement is needed for the VNIR calibration. Sensitivity degradations in the TIR bands were also confirmed by monitoring the variation in the number of saturated pixels, which were qualitatively consistent with the onboard and vicarious calibrations.

Multi-line fluorescence scanning microscope for multi-focal imaging with unlimited field of view.

Confocal scanning microscopy is the de facto standard modality for fluorescence imaging. Point scanning, however, leads to a limited throughput and makes the technique unsuitable for fast multi-focal scanning over large areas. We propose an architecture for multi-focal fluorescence imaging that is scalable to large area imaging. The design is based on the concept of line scanning with continuous 'push broom' scanning. Instead of a line sensor, we use an area sensor that is tilted with respect to the optical axis to acquire image data from multiple depths inside the sample simultaneously. A multi-line illumination where the lines span a plane conjugate to the tilted sensor is created by means of a diffractive optics design, implemented on a spatial light modulator. In particular, we describe a design that uses higher order astigmatism to generate focal lines of substantially constant peak intensity along the lines. The proposed method is suitable for fast 3D image acquisition with unlimited field of view, it requires no moving components except for the sample scanning stage, and provides intrinsic alignment of the simultaneously scanned focal slices. As proof of concept, we have scanned 9 focal slices simultaneously over an area of 36 mm2 at 0.29 µm pixel size in object space. The projected ultimate throughput that can be realized with the proposed architecture is in excess of 100 Mpixel/s.

A Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery

In recent years, anomaly detection (AD) has enjoyed a growing interest in hyperspectral data analysis. However, most state-of-the-art detectors need to work with the entire hyperspectral cube, what prevents their use for applications under real-time constraints, especially when the hyperspectral data are collected by push-broom scanners that acquire the hyperspectral images (HSIs) in a line-by-line fashion. In this paper, a Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery (LbL-FAD) is proposed, which is capable of processing each sensed line as soon as it is captured. The LbL-FAD works under the assumption that anomalous pixels cannot be well represented by the background distribution. It uses an orthogonal projection strategy for extracting a set of pixels from the first captured hyperspectral frames, i.e., lines of pixels, that are used for representing the background distribution. Using these pixels, the LbL-FAD proposes a hardware-friendly alternative to compute the orthogonal subspace to that spanned by the selected background samples, making the anomalous pixels better detectable. In addition, the LbL-FAD incorporates an automatic thresholding method which provides line-by-line and real-time binary maps where anomalous targets are segmented from the background. This novelty clearly differentiates the proposed LbL-FAD from the conventional anomaly detectors, which usually are not able to automatically discriminate anomalous pixels from background pixels until the entire image is processed. Several experiments have been carried out using real HSIs collected by different sensors. The obtained results clearly support the benefits of our proposal, both in terms of the accuracy of the detection performance and the computational complexity.

Radiometric Assessment of a UAV-Based Push-Broom Hyperspectral Camera.

The use of unmanned aerial vehicles (UAVs) for Earth and environmental sensing has increased significantly in recent years. This is particularly true for multi- and hyperspectral sensing, with a variety of both push-broom and snap-shot systems becoming available. However, information on their radiometric performance and stability over time is often lacking. The authors propose the use of a general protocol for sensor evaluation to characterize the data retrieval and radiometric performance of push-broom hyperspectral cameras, and illustrate the workflow with the Nano-Hyperspec (Headwall Photonics, Boston USA) sensor. The objectives of this analysis were to: (1) assess dark current and white reference consistency, both temporally and spatially; (2) evaluate spectral fidelity; and (3) determine the relationship between sensor-recorded radiance and spectroradiometer-derived reflectance. Both the laboratory-based dark current and white reference evaluations showed an insignificant increase over time (<2%) across spatial pixels and spectral bands for >99.5% of pixel–waveband combinations. Using a mercury/argon (Hg/Ar) lamp, the hyperspectral wavelength bands exhibited a slight shift of 1-3 nm against 29 Hg/Ar wavelength emission lines. The relationship between the Nano-Hyperspec radiance values and spectroradiometer-derived reflectance was found to be highly linear for all spectral bands. The developed protocol for assessing UAV-based radiometric performance of hyperspectral push-broom sensors showed that the Nano-Hyperspec data were both time-stable and spectrally sound.

Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification

The floral tissue of blueberry buds is very sensitive to the freezing and can be damaged during early spring freeze events. Such damage results in loss of yield to the commercial blueberry producers. Therefore, this study investigated the feasibility of injured blueberry buds damage detection during early spring freeze using hyperspectral imaging (HSI) technique. Imaged were the buds from bud swell and early pink stage for two growing seasons. Prior to imaging, buds in 2017 were subjected to freezing temperatures of −7 to −16 °C and −2 to −11 °C at respective growth stages. In 2018 season, buds were frozen at −11 to −20 °C and −4 to −13 °C at two respective sampling date for bud swell stage and at −2 to −11 °C for early pink growth stage. The HSI data was obtained using a push broom type system in the spectral wavelength ranges of 517–1729 nm. For the imaged buds, each of the indivisual buds on sampled shoot was considered as one sample, and the average spectra of such bud within 540 to 1599 nm was extracted for further analysis. Principal component analysis and successive projection algorithms were applied to select key wavelengths related to bud damage symptoms. Common wavelengths of 615, 673, 690, 756, 979 and 1467 nm were identified for both the growth stages. Partial least squares discriminant analysis was performed to identify normal and injured bud samples with the selected common wavelengths. Performance of the models manifested good results with better sensitivity (>0.80), specificity (>0.75), the area under the receiver operating characteristic curve (>0.84) and accuracy (>0.75) attributes for 2017, 2018 seasons and two seasons combined test dataset.

Spatial Distortion Assessments of a Low-Cost Laboratory and Field Hyperspectral Imaging System.

This article describes the adaptation of an existing aerial hyperspectral imaging system in a low-cost setup for collecting hyperspectral data in laboratory and field environment and spatial distortion assessments. The imaging spectrometer system consists of an ImSpector V9 hyperspectral pushbroom scanner, PixelFly high performance digital CCD camera, and a subsystem for navigation, position determination and orientation of the system in space, a sensor bracket and control system. The main objective of the paper is to present the system, with all its limitations, and a spatial calibration method. The results of spatial calibration and calculation of modulation transfer function (MTF) are reported along with examples of images collected and potential uses in agronomy. The distortion value rises drastically at the edges of the image in the near-infrared segment, while the results of MTF calculation showed that the image sharpness was equal for the bands from the visible part of the spectrum, and approached Nyquist’s theory of digitalization. In the near-infrared part of the spectrum, the MTF values showed a less sharp decrease in comparison with the visible part. Preliminary image acquisition indicates that this hyperspectral system has potential in agronomic applications.

Proposal of a Geometric Calibration Method Using Sparse Recovery to Remove Linear Array Push-Broom Sensor Bias.

The rational function model (RFM) is widely used in the most advanced Earth observation satellites, replacing the rigorous imaging model. The RFM method achieves the desired calibration performance when image distortion is caused by long-period errors. However, the calibration performance of the RFM method deteriorates when short-period errors—such as attitude jitter error—are present, and the insufficient and uneven ground control points (GCPs) can also lower the calibration precision of the RFM method. Hence, this paper proposes a geometric calibration method using sparse recovery to remove the linear array push-broom sensor bias. The most important issue regarding this method is that the errors related to the imaging process are approximated to the equivalent bias angles. By using the sparse recovery method, the number and distribution of GCPs needed are greatly reduced. Meanwhile, the proposed method effectively removes short-period errors by recognizing periodic wavy patterns in the first step of the process. The image data from Earth Observing 1 (EO-1) and the Advanced Land Observing Satellite (ALOS) are used as experimental data for the verification of the calibration performance of the proposed method. The experimental results indicate that the proposed method is effective for the sensor calibration of both satellites.

Removal of greenhouse microclimate heterogeneity with conveyor system for indoor phenotyping

Greenhouse facilities are a prevalent contemporary crop research method, attractive for their ability to achieve controlled plant growth in all seasons and more uniform environments compared to field experiments. However, the environment within the same greenhouse is still heterogeneous, where diverging regions are referred to as ‘microclimates’. The non-uniform distributions of temperature and irradiation caused by microclimates create problems that have been proven to have their impacts on the growth, production, and quality of the crops. To address these issues, a traditional greenhouse was equipped with a conveyor system controlled by a computer algorithm to more equally expose heat, and irradiation to each plant. Using a simulation analysis of the greenhouse microclimates, the algorithm minimizes the conveyor motion while exposing all plants to equivalent units of heat and radiation. In a traditional greenhouse and one other greenhouse outfitted with a novel conveyor system, one maize genotype was replicated 50 times in each facility and subjected to drought and well-watered treatments simultaneously. Plants were scanned with VNIR hyperspectral cameras with push broom scanning from both the top and side views. Results showed that the variance within both treatment groups was significantly lower in the experiment performed in the greenhouse equipped with the conveyor system (p < .05). Results also showed the greenhouse equipped with the conveyor system had better experimental performances, where the well-watered plants and drought stressed plants showed clearer differences in projected leaf area, RWC, SPAD and 21 commonly used plant indices. Therefore, this novel greenhouse phenotyping mode showed better performances with lower plant variances compared to the conventional greenhouses.

Bundle adjustment of satellite images based on an equivalent geometric sensor model with digital elevation model

High-precision georeferencing of satellite stereo images becomes a key issue in multi-source data integration, geometric processing, and analysis for many remote sensing applications. Using existing geographic data, including readily available public data, as geometric reference for georeferencing satellite imagery is an effective and feasible way to improve the positional accuracy and reduce the costs and workforce restrictions on ground control points (GCPs) acquisition. This study proposes a novel bundle adjustment approach of push broom satellite imagery based on an equivalent geometric sensor model (EGSM). As an extension of the collinearity equations, EGSM is equivalent to the rigorous geometric sensor model, whose interior and exterior orientation parameters have clear geometric interpretations. The initial values of EGSM’s parameters can be completely recovered from the RPCs and optimized within the block adjustment without using any metadata of the satellite imagery. Furthermore, a publicly accessible digital elevation model (DEM) is used as constraints in the bundle adjustment to improve the direct georeferencing accuracies without GCPs. A set of pseudo observation equations is incorporated into the EGSM associated with appropriate weights based on the estimated variance of height differences between the tie points and the DEM surface. The performance of the proposed approach was evaluated by using 143 stereo scenes of Chinese Ziyuan-3 (ZY-3) images and the global DEM data SRTM GL3, ASTER GDEM, and AW3D30. The experimental results revealed that the proposed approach could improve not only vertical accuracies but also horizontal accuracies of satellite stereo images. The geopositional accuracy after adjustment is dependent on the quality and accuracy of the reference DEM itself. With AW3D30 as exclusive controls, the horizontal and vertical root mean square errors (RMSEs) of the experimental images are reduced from 17.3 m and 2.6 m to 2.5 m and 1.5 m, respectively. The horizontal accuracy corresponds to about 1.2 pixels in the image space, and the vertical accuracy is about 0.6 pixels with the base-height ratio of ZY-3 stereo images being 0.81. The generazation ability of the approach was further proved by the experiments of WorldView-3 stereo images.

Development of an Optical Multispectral Remote Sensing System for Measuring Volcanic Surface Phenomena – Promotion Project for Next Generation Volcano Research B2 (Subtopic 2-2)

In 2016, we launched the “Promotion Project for Next Generation Volcano Research B2 (Theme B: Development of Cutting-edge Volcano Observation Technology, subtheme 2: Development of Remote Sensing Techniques for Volcano Observation), subtopic 2-2 (Development of Remote Sensing Techniques for Surface Phenomena of Volcano)” under the “Integrated Program for Next Generation Volcano Research and Human Resources Development” [1], aiming at the development of an optical multispectral remote sensing system for measuring volcanic surface phenomena. With subtopic 2-2, we are planning to develop a new observation device called a surface phenomena imaging camera (SPIC), which is technically superior to current remote sensing techniques, i.e., optical remote observation techniques used to observe volcanic surface phenomena from aircrafts or ground. We are also aiming at applying the developed observation system to quantify volcanic activities and determine volcanic eruption potentials (degrees of urgency) or branching of event trees for volcanic crises with high accuracy, contributing to better predictions of volcanic eruption transitions. To achieve the above-mentioned aims, we started the development of the SPIC by equipping it with camera-type sensors, based on preliminary analyses of the experimental observations made with the airborne spectral imaging system ARTS-SE, which consists of a pushbroom scanner and a camera system, developed by the National Research Institute for Earth Science and Disaster Resilience in FY 2015. We have already developed its components, such as the prototype filter-type multiband cameras SPIC-UC, a prototype uncooled infrared camera, SPIC-C, a cooled camera, and SPIC-SS, a visible-light camera. The SPIC-UC is a two-band camera with the function of visualizing temperature and SO2 gas concentration distributions. The SPIC-C has the function of measuring temperatures between 2 and 1075◦C with high accuracy (noise equivalent temperature difference, NETD: 16 mK); it is equipped with a sensor and a filter wheel that work in the middle wave infrared region (MWIR). The SPIC-SS is a six-lens multiband camera system that estimates the measured images from multiband spectra (6 bands) to hyper spectra (300 bands). Further, we studied a method to estimate digital surface model with a ∼30-m error. As our plan has progressed as scheduled, we intend to complete the prototype SPIC by 2020.

CALIBRATING PHOTOGRAMMETRIC AIRBORNE CAMERA SYSTEMS WITH DIFFRACTIVE OPTICAL ELEMENTS

Abstract. This paper presents a laboratory approach for geometric calibration of airborne camera systems. The setup uses an incoming laser beam, which is split by Diffractive Optical Elements (DOE) into a number of beams with precisely-known propagation directions. Each point of the diffraction pattern represents a point at infinity and is invariant against translation. A single image is sufficient to allow a complete camera calibration in accordance with classical camera calibration methods using the pinhole camera model and a distortion model. The presented method is time saving, since complex bundle adjustment procedures with several images are not necessary. It is well suited for the use with frame camera systems, but it works in principle also for pushbroom scanners. In order to prove the reliability, a conventional test field calibration is compared against the presented approach, showing that all estimated camera parameters are just insignificantly different. Furthermore a test flight over the Zeche Zollern reference target has been conducted. The aerotriangulation results shows that calibrating an airborne camera system with DOE is a feasible solution.

Destriping Algorithms Based on Statistics and Spatial Filtering for Visible-to-Thermal Infrared Pushbroom Hyperspectral Imagery

Following calibration, hyperspectral images remain affected by spatial dimension nonuniformity, i.e., stripe noise, due to stray light interference, slit contamination, and instrument instability. The full spectrum airborne hyperspectral imager (FAHI) is a Chinese next-generation pushbroom sensor with a spectral range covering the visible near-infrared, shortwave-infrared (SWIR), and thermal infrared regions with spectral sampling intervals of 2.34, 3, and 32 nm, respectively. However, the residual stripe noise remains in FAHI images after relative radiometric correction based on the laboratory calibration, especially in low signal-to-noise ratio bands. To solve this problem, a new technique combining image statistics and spatial filtering algorithms has been developed for FAHI image correction. In this method, image statistics are obtained to calculate the gain and offset of each pixel for image nonuniformity correction. Then, a spatial filter removes the residual stripes. This paper presents the principles of this method along with details of validation experiments and results. To validate the effectiveness of the proposed method, comparison with two destriping methods and quantitative analyses are carried out. Moreover, the method is applied to an SWIR hyperspectral image from the TianGong-1 spacecraft, yielding good results. The experimental results suggest that the proposed method is convenient and practical for improving the relative radiometric accuracy of airborne/spaceborne hyperspectral images.

Cavity solitons in VCSEL: a single pane solution of optical information technology

Cavity soliton (CS), a unique class of localized pattern inside a wide-area, driven cavity system is investigated from the perspective of mathematical modeling as well as applications in optical information technology. Possibility of generation of CSs is explored in different cavity systems; all having a vertical cavity surface emitting laser as the key component. Parametric space for the stability of the CS and the CS clusters is demarcated. The potential of using such CS as future ‘bits’ of information is elaborated. Schemes are developed to demonstrate how the cluster formation of CSs can be used to realize all-optical multilevel data encryption system. The role of nonlinear saturable absorber for the generation of CS is highlighted. In certain operating conditions the CSs exhibit spontaneous movement that basically owes to the gradients present in the system. Interaction dynamics of two or more CSs are investigated. Besides the regular ones the CSs show-off few unique dynamics like ‘all-optical push broom’ and ‘all-optical-marching’. The possibility and challenges to utilize the gradient-driven CS dynamics for the realization of ‘on-chip’ microscope are discussed.

Multi-scale ghost imaging LiDAR via sparsity constraints using push-broom scanning

Considering prior statistical information about multi-scale of a target scene, a multi-scale ghost imaging LiDAR via sparsity constraints using push-broom scanning is proposed in this work. This method significantly improves the imaging efficiency. Simulation and experimental results demonstrate that it has higher imaging quality at low sampling rate than using single-scale speckle pattern.

Ghost imaging LiDAR via sparsity constraints using push-broom scanning.

Ghost imaging LiDAR via sparsity constraints using push-broom scanning is proposed. It can image the stationary target scene continuously along the scanning direction by taking advantage of the relative movement between the platform and the target scene. Compared to conventional ghost imaging LiDAR that requires multiple speckle patterns staring the target, ghost imaging LiDAR via sparsity constraints using push-broom scanning not only simplifies the imaging system, but also reduces the sampling number. Numerical simulations and experiments have demonstrated its efficiency.

Calibration model of polarimeters on board the Aerosol-UA space mission

The polarimetric model has been developed for calibration of the multispectral Scanning Polarimeter (ScanPol) and the MultiSpectral Imaging Polarimeter (MSIP) of the Aerosol-UA space mission. ScanPol and MSIP polarimeters are designed to provide high precision measurements of the atmospheric/earth surface radiation polarization with a substantial cross-track spatial coverage. The main sources of the instrumental errors, such as the finite extinction ratios of polarizers and the phase retards of telescopes are simulated in the polarimetric model. The mirrors mismatching, dark reference signal, the difference in the polarimeter channels gain, and the instrumental depolarization factor are modeled as additive values and scalar multipliers. The polarimetric model is derived in the form of Mueller matrices, which describe the transformation from the incoming polarized radiation to the measured signal. The parameters of the model are estimated experimentally. The numerical experiments have been carried out to validate the effectiveness of the proposed calibration procedures. The results have shown the possibility to minimize the ScanPol polarization errors below 0.15% (DoLP) and 0.2° (AoLP), and the MSIP polarization errors below 1%. The set of two polarimeters, namely the along-track scanner and the wide-angle imager, allows providing the in-orbit cross-calibration of the MSIP using the ScanPol measurements when their fields of view are overlapped. The concept of the MSIP and ScanPol polarimeters cross-calibration is discussed in the paper.

Non-invasive and rapid pH monitoring for meat quality assessment using a low-cost portable hyperspectral scanner

It has been demonstrated that optical spectroscopy is a powerful tool for the quantitative monitoring of the main chemical components in food. However, portable spectrometer for on-site food quality assessment has rarely been reported. Here, a low-cost and portable hyperspectral scanner is developed. Utilizing this hyperspectral scanner by handheld push-broom scanning, reflectance spectra of meat samples can be obtained non-invasively and rapidly. Support vector regression (SVR) model is used to predict the pH value. The prediction accuracy rate of the model is close to 90%, and the coefficient of determination is about 0.93, which shows the feasibility of this system in on-site monitoring pH of meat.

Study of modeling optimization for hyperspectral imaging quantitative determination of naringin content in pomelo peel

With the development of modern computational science and data metrology, hyperspectral imaging technology has been utilized in the field of remote sensing for the application in precision agriculture and crop quality testing. In this paper, the near infrared hyperspectral imaging (NIRHSI) technique was applied to quantitatively determine the naringin content in pomelo peel samples. Model optimization was studied by investigating the influence of system measuring conditions and parameters of the built-up HSI instrumental system. The HSI data acquisition was built up using a prevalent pushbroom scanner in the near infrared region. Calibration models were established using the partial least squares (PLS) regression in the mode of cross validation, in combined use of the Savitzky-Golay smoother (SGS) for data pretreatment. These multivariate analytical models were optimized in comparison about the region of interest (ROI) for NIRHSI model optimization. In the process of NIRHSI data acquisition, the rational values of some system measurement parameters were also tuned and tested, such as the use of different watts for light intensity, different lenses and different materials as the scanning backgrounds. Results showed that the cross-validation PLS regression methods performed well in the calibration and prediction processes, working well together with the parameter tuning of the SGS pretreatment. In addition to the fact that different materials as the scanning backgrounds obviously affected the quantitative result, there is no apparent difference in the comparing cases of different light intensities and different lenses. This work validates the capability of applying NIRHSI technique to quantitative determine the content of naringin in pomelo peel samples. The test for model optimization by comparing the measurement parameters and the system properties has prospective application ability in fields of other spectral/hyperspectral data analysis. It is an important lab simulation of remote sensing. It contributes significant theoretical reference to the design of the large-scale online hyperspectral data acquisition systems.

Hyperspectral Push-Broom Microscope Development and Characterization

Currently, the use of hyperspectral imaging (HSI) for the inspection of microscopic samples is an emerging trend in different fields. The use of push-broom hyperspectral (HS) cameras against other HSI technologies is motivated by their high spectral resolution and their capabilities to exploit spectral ranges beyond 1000 nm. Nevertheless, using push-broom cameras in miscroscopes imposes to perform an accurate spatial scanning of the sample to collect the HS data. In this manuscript, we present a methodology to correctly set-up a push-broom HS microscope to acquire high-quality HS images. Firstly, we describe a custom 3D printed mechanical system developed to perform the spatial scanning by producing a precise linear movement of the microscope stage. Then, we discuss how the dynamic range maximisation, the focusing, the alignment and the adequate speed determination affect the overall quality of the images. Finally, we present some examples of HS data showing the most common defects that usually appear when capturing HS images using a push-broom camera, and also a set of images acquired from real microscopic samples.

推扫式高光谱相机自动调焦

推扫式高光谱相机自动调焦