Airborne optical imaging and measurement technology has prominent advantages such as high timeliness, low cost, high resolution and easy interpretation. It has been widely applied as an indispensable optical remote sensing method. Due to the influence of aviation flight, in order to obtain clearer optical images and more accurate measurement, the problems should be solved such as aerodynamic, vibration, temperature and pressure changing under the constraints of limited volume, weight and power consumption. To meet the demand of new technologies, new methods and new theories in this field, a special issue of airborne imaging and measurement was organized. A number of excellent research results were introduced in the fields of precision optics, precision mechanics, precision control and image processing. It promotes the further development of relative research from the perspective of theoretical guidance and engineering reference value.

To analyze the influence of aeronautical low temperature on the self-collimation focus detection system, and enables the design and correction of the actual focus detection system, the relationship among various factors under different imaging conditions was investigated. Based on the principle of autocollimation focus detection and geometry, comparison of single-point and fringe imaging, imaging position and width according to different received light intensities, and presence and absence of astigmatic conditions were analyzed. Thus, a related analysis formula was deduced.Combined with the actual camera working conditions, the corresponding simulation of the optical system was performed, and the simulation results (simulated bimodal voltage values of 1.4 V and 0.47 V) were in accordance with the actual test data (peak-to-focus double-peak voltage values of 1.38 V and 0.56 V). The actual modification measures are carried out to improve the temperature adaptability of the self-collimation detection system.The temperature adaptability analysis is in accordance with the actual demand and has a guiding function for the temperature adaptability of the system.

To address issues such as auto-focusing system fault analysis and component selection, and to improve the focusing accuracy of aerial remote cameras, this article offered insights into the auto-collimated focusing technology based on the Ronchi grating.First, in this paper,several common focusing methods of both domestic and foreign aerial cameras were summarized and compared. Then, the composition and operating principle of optoelectronic auto-collimated focusing systems based on the Ronchi grating were introduced, the mathematical model was established, and the simulation curves of the focusing waveform with different imaging positions were presented. To measure the focal distances, the concept of the defocusing evaluation factor was proposed.Next, the three factors that affect the focusing accuracy of the diffraction effect and the aberration of the optical system, the period selection of the Ronchi grating, and the energy distribution of the light source were analyzed. The influence of the Ronchi grating period selection method was then presented. Finally, the experimental results verify the validity of the simulation results and show that the peak-to-peak ratio of the focusing waveform is 0.82, corresponding to an average focusing detection resolution of 10% at 0.05 mm.

Using a tethered balloon as a flight platform for a hyperspectral camera, a two-axis stable platform was designed to compensate the attitude change of the platform in the heading and pitching directions. The azimuth and pitch driving mechanisms of the stable platform were powered by a cable drive. The continuous driving torques of the azimuth and pitch axes were 93.6 N?m and 117 N?m, respectively, and the maximum rotational speeds of the shafts were 25 (°)/s and 20 (°)/s, respectively. The test results show that the transmission accuracy of the stable platform is 5 μrad, and the transmission error is less than 0.7%. The servo bandwidths of the azimuth and pitch axes are 15 Hz and 35 Hz, respectively. The root mean-squared (RMS) errors of the sinusoidal tracking accuracies of the azimuth and pitch axes are 0.0045 and 0.0043, respectively. An outfield flight test was performed with a 35-kg hyperspectral camera mounted on the stable platform pitching frame to conduct air-to-earth observations, and the gyro stability data of the flight test was monitored. The RMS stability accuracy of the azimuth axis (38.83 μrad) and the pitch axis (37.26 μrad) met the 50-μrad requirement of the hyperspectral camera.

This study analyzes The effects of aircraft's attitude, speed, and height on the overlap of an obliqueaerial camera was analyzed in this study, and acorresponding compensation method was developed. The calculation of oblique imaging overlap using coordinate transformation was introduced based on ageo-location algorithm and the Gauss-Kruger projection. In the case of single parameter variations, different formulas were provided by the geometric method, and the results were consistent between coordinate transformation and geometric methods. In the case where the three parameters varys imultaneously, the geometric method did not work adequately, and the coordinate transformation method should be used. In the case where the varied angle value of the aircraft attitude was below 1°, the deviating value between real covered area and expected area was attained by thecoordinate transformation method. The usage of camera's azimuth and pitch angles to compensate the effect of aircraft attitude on overlap was introduced. For the variation of aircraft speed and height, changing the imaging period according to the aircraft real time values was suggested, and a new formula regarding imaging period was provided.The validity of the compensation algorithm was verified by simulation and real flight. The result shows that when the camera's field of view is below 0.88°，the deviation between mean and expected values of real overlap is approximately 1%, if the target area is flat.

To solve the problem of large geo-location error in the application of ground target geo-location in long-distance aviation imaging with large inclination angle, a systematic error corrected geo-location was proposed. The systematic error analysis and modeling of the long-distance aviation imaging system with large inclination angle was carried out. In view of the difficulty in obtaining the residual error, a method for estimating residual error parameters based on ground control points was proposed.The simulation results show that the residual error can be reduced to 1/10 by estimating residual error parameters based on ground control points. Flight test data confirm that the mean geo-location error can be reduced from 401 m to 97 m by correcting the systematic error in the positioning of targets in long-distance aviation imaging with large inclination angle. Thus, the systematic error corrected geo-location method can effectively improve the accuracy of ground target geo-location in the application of long-distance aviation imaging with large inclination angle.

A highly integrated optical imaging system with a wide field of view (FOV) and high resolution was proposed to meet the performance requirements of modern photoelectric imaging systems. The system consisted of a concentric spherical lens and an array of micro-cameras arranged in the Galilean concentric multiscale structure. A wide FOV was achieved owing to the structure of the spherical lens. The multiscale micro-camera array enabled capturing of images at high resolution and with a wide field of view. Further, the Galilean structure made the system more compact. In addition, the arrangement of the camera array was designed to reduce the number of cameras and realize a lightweight system. By optimizing the design of the whole system, the modulation transfer function (MTF) can reach 0.3 at a frequency of 270 lp/mm.The root mean square (RMS) radius of the full field of view of the system is also smaller than the detector pixel size of 1.85 μm, indicating an excellent imaging effect from the system. Moreover, results of the tolerance analysis indicate that the system is easy to manufacture. Thus, the system not only realizes a wide field of view and high- resolution imaging, but also has low structure complexity and a compact volume structure.

To address the demand for detection of point/dim targets in complex environments, an infrared dual-band dual-field of view (FOV) imaging early warning system was designed. To improve the target detection capability and environmental adaptability, the system used high-order aspheric surfaces to reduce the number of system lenses and improve the system transmittance. At the same time, it corrects on-axis/off-axis aberrations and advanced aberrations to improve the imaging quality of the system. An optical passive compensation method was adopted to realize an athermalized design in the range of -40 ℃ to 60 ℃. A rotating electromagnet was used as the driving element to complete the zooming in process in a duration of 80 ms to ensure that the target is not lost during the FOV switching process. Using the electric limit, mechanical limit, and magnetic locking mechanism as limit components, the stabilization accuracy of the optical axis wobble is less than two pixels.The design results show that the optical-mechanical structure of the infrared imaging early warning system is reasonable and compact, the imaging quality is good, and it meets the requirements of target detection.The system has potential application prospects in the field of infrared imaging in early warning systems.

In the existing action distance model of the infrared system,the influence factors are not thoroughly considered.Moreover,it is difficult to accurately evaluate the detection ability of the system and the infrared stealth effect of the target.Therefore, to address these issues,the action distance model and an application case analysis of the air-based infrared system were presented in this work.Based on the analysis of the influencing factors of the typical working distance of the infrared system, the implicit function of the atmospheric spectral transmittance and the working distance were defined. The distribution model of the spectral radiation intensity was clarified based on the temperature difference between the vertical layers of the atmosphere.The weighted correction coefficient of the atmospheric transmittance was deduced, considering the characteristics of the infrared spectral radiation line of the target.Next, the model of the distance between the air-based infrared system and the point target was built, which included the characteristics of the target spectral radiation, the atmospheric temperature, and the height of the infrared system. Based on this model, the detection capability of different types of air-based infrared systems and the infrared stealth performance of different types of targets were compared and analyzed. The results show that for aircraft and missile targets, the design of a large-aperture long-wave infrared system is helpful in enhancing the detection ability of the air-based infrared system regarding the head-on direction of the target.Furthermore, the large-aperture mid-wave infrared system has a stronger detection ability regardingthe rear direction of the target.The infrared stealth and penetration ability of a small-size, low-surface-emissivity, and low-speed flying target is better.Finally, the stealth performance of a target in the stratosphere is better than that of a target in the troposphere and that of a middle-layer flying target.

Aiming to determine the polarization characteristics of the sea surface in the infrared wave band, a model of the polarization bidirectional reflection distribution function (pBRDF) for sea surface micro-elements was developed based on the micro-facet BRDF. Considering the effects of emission and reflection from a sea surface on the radiation received by the detector during radiation transmission, a novel model of sea surface infrared polarization characteristics was proposed. The elevation and slope of the sea surface could be captured using the Elfouhaily wave spectra and fast Fourier transforms (FFTs). Then, the degrees of linear polarization of sea surface emission and reflection radiation for different observation and incident zenith angles and for different wind speeds were calculated.Infrared polarization images of the sea surface and a ship were simulated. Through a comparative analysis of simulated data and the data obtained from the literature, the results show that the model established in this paper is suitable for investigating the infrared polarization characteristics of the sea surface. Compared with infrared images based on light intensity information, the infrared polarization images provide more details about the sea surface. Meanwhile, the differences in polarization characteristics between the target and sea surface are clearer, and the contrast between the sea surface and target is higher. The model proposed in this paper is of significance in the detection of maritime targets in the infrared wave band.

In the Window Fourier Transform (WFT) phase extraction technique, there is a contradiction between suppressing the linear phase error and background intensity interference on the window size, and it is impossible to suppress both simultaneously. Therefore, a linear phase error suppression technique based on changing the spectral components of the input signal was proposed. The Fourier space-frequency analysis method was used to filter the input signal and preserve the fundamental frequency components.This ensured that the measurement results were not affected by the background intensity. Considering that the linear phase error was also affected by the window size selection, the optimal window size was determined by simulation analysis.Finally, the parabolic shape was reconstructed by this method and compared with the surface shape obtained by the phase-shifting. The results show that compared to the traditional phase extraction technique, the proposed method achievesa comprehensive suppression effect of the linear phase error and background intensity interference.Moreover, it improves the traditional WFT phase extraction accuracy.

This study systematically analyzed and compared various snapshot imaging techniques.According to the method of spectral data cube segmentation in 2D space, snapshot spectral imaging techniques are divided into four categories: image segmentation, aperture segmentation, optical path segmentation, and frequency segmentation. The principles, advantages, disadvantages, and current status of 17 technical schemes werestudied for each category.The analysis and comparisonwere based on the following five aspects: the tradeoff between the spectral channels and spatial pixels, matching of space and spectrum, space continuous sampling, light utilization, and detector dynamic range.The results of our study will help researchers in related fields to quickly and comprehensively understand the research status of snapshot spectral imaging, and in addition the results will lay a foundation for further improvement of the overall performance of these techniques.

By using a gyroscope to directly measure the inertial angular velocity of the load inside a photoelectric platform to construct feedback, stable imaging can be achieved by controlling the inertial angular velocity of light of sight on the moving carrier. The gyroscope strapdown inertial stability control method can be used to construct the feedforward, increase the system bandwidth effectively, and minimize control error. However, there are requirements for the gyroscope installation position. We have proposed an equivalent strapdown stability control method, which satisfies the mechanical installation conditions with direct gyroscope feedback. The method establishes a dynamic model considering the constraints of the mounting base. The model reveals the resonance problem caused by the installation stiffness of the photoelectric platform base. For a pair of resonance and antiresonance, the filter to eliminate resonance based on a stable pole-zero cancellation method was designed. An equivalent strapdown inertial stability loop was constructed with the inertial angular velocity of the frame measured using the gyroscope, and the relative rotation angle of the mechanical frame measured using an encoder. In the loop above, we combined the inner loop interference suppression with the compound control method usingthe inverse model feedforward, which successfully expanded the system bandwidth, improved the tracking precision of the command, and enhanced the isolation performance. The simulation and experimental results show that the proposed method can effectively suppress the resonance of the elastic restraint moment of the mounting base and exhibit better performance than the gyroscope direct feedback control. For tracking a typical sinusoidal angular velocity command with an amplitude of 1 (°)/s and a frequency of 1 Hz, the root mean square error of the system decreased from 1.75 to 0.23 (°)/s, and the disturbance isolation decreased from 18% to 2%.

In this study, a composite control algorithm was developed for controlling the turntable speed of a circumferential scanning imaging system (ICSIS) driven by a permanent magnet synchronous motor (PMSM) to obtain stable high-resolution images. Based on the load characteristics of the turntable and the mathematical model of the PMSM, a single-sampling rate control system model, comprising the mechanical parameter uncertainty and fast-changing torque disturbance, was established. The fast nonsingular terminal sliding mode (FNTSM) control and an extended high-gain observer were used in designing the speed-tracking controller. The maximum torque current ratio control was determined through another FNTSM control. Finally, the performance of the speed tracking control based on the above composite algorithm was analyzed and verified. The experimental results show that when the turntable speed is set to 120 or 240 r/min, the speed tracking error is less than 0.1%. Compared with the proportional-integral control, FNTSM control, and linear sliding mode control+observer, the governing system with the proposed algorithm was characterized by no overshooting, stronger anti-disturbance, and higher speed-tracking precision, which enabled the ICSIS to capture clear and stable circumferential images.

A reduced-order autodisturbance rejection control method was proposed to improve the control performance of a fast steering mirror(FSM) driven by VCA applied in aerial photoelectric loads. The FSM model was analyzed, and the model parameters were obtained. Based on the theory of active disturbance rejection control (ADRC), the general third-order ADRC of the FSM was designed. The eddy current sensor measurement results were assumed, and the reduced-order extended state observer and its corresponding ADRC design method were proposed. Based on the controller bandwidth design theory, the control law of the second-order under damped object, such as FSM, was deduced, and the specific realization form of the control law with the disturbance compensation value was provided. The experimental results show that the reduced-order ADRC can significantly improve the dynamic performance of the positional step response of the FSM and can achieve a step response without overshoot and oscillation. The steady-state time reduces from 11.7 to 9.2 ms.In addition, the tracking steady-state error of the position ramp response declines, and the dynamic process of speed response improved. The speed stabilization time of image motion compensation drops from 10.2 to 7.8 ms, which is approximately 24%. The reduced-order ADRC can significantly improve the dynamic performance of FSM because of its simpler implementation and less computation.

To improve the accuracy and real-time performance of infrared (IR) dimsmall target detection, an IR dimsmall object detection algorithm based on an improved multi-scale fractal feature was presented.Computational analysis of the multi-scale fractal feature related to the fractal parameter K (MFFK), which was used for IR image enhancement in the algorithm,was performed. First, an improved multi-scalefractal feature (IMFFK) was presented to perform image enhancement after substituting the equation for computing fractal dimension into the equation for computing MFFK using the covering-blanket method. Thereafter, a computationally efficient IR dimsmall target detection algorithm was presented, in which the computation of IMFFK was simplified and an adaptive threshold was used to segment targets of interest from the background. Finally, the effect of primary parameters on image enhancement and computational cost was analyzed based on the simulation images. The IR real-world images were subsequently used to evaluate the detection performance of the proposed algorithm, and comparisons with state-of-the-art detection algorithms based on local contrast measureare performed. The proposed algorithm was capable of simultaneously detecting dimsmall and large targets in an IR image, irrespective of whether the targets were bright or dark, even though false alarms were detected in some scenarios. It is also capable of reachingapproximately 30 frames per second for low-resolution IR images (320×240). The proposed algorithm exhibitssatisfactory applicability and can be used to detect targets with high local contrast in an image.

Most existing dehazing algorithms suffer from under-or over-enhancement, color distortion, and halo artifacts. An improved method of atmospheric light estimation using quad-tree subdivision and an improved guided filter were proposed to solve these problems. First, a more faithful estimate of global atmospheric light was produced by quad-tree subdivision using a non-overlapped dark channel. Then, the reasons for the existence of halo artifacts in edge regions were discussed and an adaptive weight was added to the guided image filter. The improved guided image filter was used to refine the raw transmission map. Finally, based on the atmospheric scattering model, a dehazed image was obtained using the estimated atmospheric light value and refined transmission map. Experimental results indicate that the color of the dehazed image is more reliable and halo artifacts in edge regions are reduced. The proposed algorithm performs better than state-of-the-art haze removal algorithms in terms of color fidelity and detail enhancement.

Ship detection from optical remote sensing images is easily disturbed by complex backgrounds, such as clouds, islands, and sea clutter. In this paper, a novel ship detection method was proposed to solve these problems. First, to solve the change in target size, multi-scale saliency maps were generated using a spectral residual visual saliency model, and the optimal saliency map was adaptively selected using the Gini index. Further, considering the problem of missing detection caused by the threshold segmentation, a two-stage segmentation method was proposed to separate the target and background pixels. The local maximums of the saliency map were then obtained using image expansion, and the k-means algorithm was adopted to determine whether each local maximum belongs to the target pixel or background pixel. The accurate candidate locations were obtained using the local threshold segmentation. Finally, the rotation invariant feature based on the radial gradient transform was introduced to further eliminate false alarm. The experimental results show that the proposed detection method can successfully detect ship targets of different sizes and directions and effectively overcome complex background interference. Additionally, the detection accuracy is 93%, and the false alarm rate is 4%, which are better than other saliency- based ship detection methods.

Due to the complexity of the background in aerial images and the diversity of object categories, aerial image classification is a challenging task. In order to address the problems of low accuracy and poor generalization in traditional multi-label aerial image classification methods, a method based on recurrent neural networks was proposed.In this method, the super-pixel segmentation algorithm was first used to obtain the low-level features of the image from which an attention map was generated. Subsequently, the best image scale was obtained by cross-validation,and multi-scale attention feature graphs were embedded into aconvolutional neural network in order to extract the features of the image.Finally, tomine the correlation between labels,an improved bidirectional Long Short-Term Memory (LSTM)network was proposed, which increases the connection from the input gate to the output gate, so that the input state can efficiently control the output information of each memory unit. The forget gate and the input gate were combined into a single update gate so that the improved bidirectional LSTM network can learn long-term historical information. The results obtained by applying the proposed method to the UCM multi-label dataset indicate that for scale values of 1,1.3, and 2, the accuracy and recall rates of the model are 85.33% and 87.05% respectively,while the F1 score reached 0.862. The accuracyand recall rates are found to be higher than those of theVGGNet16 model by 7.25% and 8.94% respectively.The experimental results thus indicate that the proposed method can effectively increase the accuracy of multi-label aerial image classification.