Multiple Image Sensors Research Articles

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato.

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to revealthe dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.

Transmission mode synthetic aperture interferometry using a compact coherent imager and switched fiber-optic illumination

This paper discusses a modular implementation of synthetic aperture interferometry (SAI) using inexpensive telecommunications technologies that can be expanded for use in optical instruments with arbitrarily large fields of view. Exploiting multiple image sensors and multiple illumination sources, transmission and reflection SAI geometries, capable of emulating the phase contrast and interference modes of conventional interference microscopes, are considered. The point-spread and transfer characteristics of these geometries are illustrated for the case of a transmission mode instrument. It is shown that although imaging is strictly field dependent (i.e. shift-variant), the response of modular instruments is periodic and with careful choice of illumination and sensor positioning can be made quasi shift-invariant. We demonstrate SAI in a transmission configuration comprising of a single coherent imager that holographically records the field scattered from a test target sequentially illuminated by 16 different plane waves. This set-up is equivalent to a single module of a larger SAI system and since brightfield regions in the individual reconstructed images overlap, provides an ideal platform to examine the effect of tilt and phase offset errors in the illumination. By applying a first order correction and assuming phase continuity over the overlapping regions, we demonstrate a significant improvement in the SAI reconstruction.

Design and Performance Analysis of the Highly Sensitive Deep Vacuum Cooling sCMOS Imaging System for Highly Sensitive Detection of Space Targets

The sCMOS imaging system with deep vacuum cooling technology has become a necessary way to improve the detection capability of space targets. In order to improve the detection capability of the photoelectric detection equipment for space targets, this paper developed the Highly Sensitive Deep Vacuum Cooling Imaging System (HSDVCIS). Firstly, we designed the imaging readout processing circuit using the GSENSE4040 sCMOS image sensor designed and manufactured by Gpixel and the deep vacuum cooling structure using thermoelectric cooling. Then, we tested the designed HSDVCIS with readout noise, dark current, and dynamic range of 3.96 e−, 0.12 e−/pixel/sec, and 84.49 dB, respectively, and tested the image sensor with a minimum cooling temperature of −40 °C. Finally, according to the results of observation experiments, we validated that the photoelectric detection equipment equipped with HSDVCIS improved the limiting detection magnitude (at SNR = 5 level) from 13.22 to 13.51 magnitudes within a 3 s exposure time by turning on the cooling function. Therefore, HSDVCIS designed in this paper can achieve highly sensitive detection of space targets. At the same time, the development of HSDVCIS also provides technical reserves and strong support for future research on the imaging systems using multiple image sensor mosaics.

Possible approaches for processing of spherical images using SfM

Spherical cameras are being used more frequently in surveying because of their low cost and possibility to process spherical images with conventional SfM methods. Fish-eye lenses are now frequently found on modern 360° cameras, allowing for the capture of the entire scene and subsequent model reconstitution by a non-photogrammetrist specialist. Gyrospherics are a feature of cameras that ensure image stabilization to reduce blur when the camera is moving. This feature allows it to capture moving scenes in time-lapse mode. However, two main factors - hardware parameters and software algorithms - affect the quality of the images that are captured. The 360° camera design allows for a variety of data processing methods by SfM. These images were created using multiple image sensors and lenses from each 360° camera. These images can be processed individually or by applying rules to define relationships between images. Also, images from cameras can be stitched and processed with a spherical camera model. In this paper is proposed processing methods of data from 360° cameras and estimated accuracy of each method.

DRT: Deformable Region-Based Transformer for Nonrigid Medical Image Registration With a Constraint of Orientation

Nonrigid medical image registration is a key technology for clinical disease diagnosis by integrating the vital information taken from input images acquired using multiple imaging sensors. Recently, transformer networks have attracted attention in image registration, but splitting images into patches remains a problem. Although there are usually multiple structural regions with different sizes and shapes in medical images, current transformer methods usually use an equal-sized or rectangular patch embedding that cannot represent the structural differences and directionality of different regions. In addition, it may also destroy the semantic consistency of objects. To address this problem, we propose a new deformable region-based structural relevance embedding (DRSRE) module which learns to adaptively split the images into deformable structural regions of different sizes and shapes with orientation constraints rather than using equal-sized or rectangular patches. In this way, our method can represent the structural differences and directionality between regions and effectively preserve the semantic consistency in regions to calculate accurate transformation relationships for nonrigid image registration. We term the DRSRE module with a transformer part as a deformable region-based transformer (DRT) and conduct extensive evaluations of DRT in the context of nonrigid medical image registration, and the results validate that the proposed DRT outperforms the state-of-the-art nonrigid registration methods, achieving 0.748 and 0.886 Dice scores with 0.217% and 0.308% foldings for deformation field on the LPBA40 and ADNI datasets.

Analysis Changes in Mangrove Forest Cover Using Multi-Sensor Image in North Luwu District South Sulawesi 2015-2020

Mangrove forest is one of the essential components of natural ecosystems. Mangrove forests have various essential functions, such as holding land sediments, tsunamis, and ocean waves, storing large amounts of carbon, and providing other benefits for coastal and land areas. However, the conversion of mangrove forests has reduced and degraded mangrove land. Therefore, monitoring and conserving land changes in mangrove forests must be carried out to determine the effects on land ecosystems and coastal areas. Remote sensing has the spatial ability to analyze changes in mangrove ecosystems in coastal regions temporally because it has the advantage of using satellite imagery data. This study compares the classification method using multiple image sensors to analyze land cover changes in mangrove forests in North Luwu Regency, South Sulawesi, in 2015-2020. The technique used in this research is the classification of Object-Based Image Analysis (OBIA) and the variety of Maximum Likelihood. The results of Sentinel-1 image analysis using Maximum Likelihood provide information on changes in mangrove land cover during 2015-2020 with an area of 449.17 (Ha), while the results of Landsat 8 analysis using (OBIA) provide information on changes in mangrove land cover 596 (Ha).Keywords: Optics, Radar, OBIA, Maximum Likelihood, mangrove

Progressive Early Image Recognition for Wireless Vision Sensor Networks.

A wireless vision sensor network (WVSN) is built by using multiple image sensors connected wirelessly to a central server node performing video analysis, ultimately automating different tasks such as video surveillance. In such applications, a large deployment of sensors in the same way as Internet-of-Things (IoT) devices is required, leading to extreme requirements in terms of sensor cost, communication bandwidth and power consumption. To achieve the best possible trade-off, we propose in this paper a new concept that attempts to achieve image compression and early image recognition leading to lower bandwidth and smart image processing integrated at the sensing node. A WVSN implementation is proposed to save power consumption and bandwidth utilization by processing only part of the acquired image at the sensor node. A convolutional neural network is deployed at the central server node for the purpose of progressive image recognition. The proposed implementation is capable of achieving an average recognition accuracy of 88% with an average confidence probability of 83% for five subimages, while minimizing the overall power consumption at the sensor node as well as the bandwidth utilization between the sensor node and the central server node by 43% and 86%, respectively, compared to the traditional sensor node.

Object detection and classification for small objects in/on water

The ability to quickly detect and identify objects floating on the surface of the water can benefit numerous fields including environmental monitoring, civil infrastructure, and security applications. To address this need, we investigated the ability of multiple imaging sensors to perform machine learning-based object detection and classification for small objects on water. The sensors tested in this effort include a long-wave infrared polarimeter camera, a visible wavelength optical camera, and a low-light camera. Small waste objects such as trash bags, Styrofoam cups, wood, plastic bottles, cardboard, and aluminum cans were placed into a riverine environment on the water surface and data were gathered with each of the sensors. Artificial neural networks were trained based on the data gathered, and models were created to perform object detection and classification of those same objects in the riverine environment. The three-camera systems performed well but with clear advantages for specific environmental conditions and object types.

Spatial Feature-Based Prioritization for Transmission of Point Cloud Data in 3D-Image Sensor Networks

A sensor network that connects multiple 3D image sensor devices using light detection and ranging (LIDAR) is effective for collecting spatial information to detect potential risks associated with people’s movements. However, due to the large volume and high frame rate of point cloud data obtained by LIDAR devices, under the condition of strictly limited bandwidth, some part of the data can go missing when detection is performed from point cloud data, causing a degradation of the accuracy. As the communication traffic volume of other applications increases, the communication available bandwidth for spatial information applications becomes insufficient. This paper proposes a scheme of spatial feature-based prioritization for the transmission of point cloud data with strictly limited communication bandwidth. It estimates how much each piece of point cloud data contributes to the accuracy improvement of a machine learning task for spatial prediction and assigns a higher score to those with a larger contribution. These scores prioritize the control for the transmission of point cloud data from sensor devices to an edge server. Feature selection with machine learning is helpful for estimating data importance. To improve the estimation accuracy, the proposed scheme complements missing entries when performing feature selection. We developed an indoor experimental environment and found that the prediction accuracy was sufficiently maintained compared to two benchmark schemes, random and heuristic, with limited communication bandwidth. Comparison with the perturbation method, which is a conventional method of feature selection, demonstrated the effectiveness to complement missing values in the proposed scheme.

An innovative distributed filter for airborne distributed position and orientation system

Airborne earth observation with multiple imaging sensors requires high-precision motion information of multiple nodes on aircraft wings. Distributed position and orientation system (DPOS) is an important way to meet this acquirement, in which the high-precision motion parameters of sub-nodes are calculated by transfer alignment. Due to the complex elastic deformation between the nodes and the different accuracy of deformation data, the transfer alignment accuracy of each sub-node is inconsistent. Focus on this problem, a novel deformation measurement method based on accelerometer and an innovative distributed filter are proposed. Firstly, the wing deformation is calculated by relationship of the acceleration between master node and sub-node. The deformation data includes bending angle and deflection. Then a novel distributed filter is conducted, which includes three parts. The first part is to determine the relation model of motion information between different nodes. This relation model is calculated based on the above deformation data. The second part is sensor-level inertial measurement fusion, which can use the accelerometer data with small bias to amend gyroscope data with serious drift and improve sensor data precision of all nodes. The third part is system-level navigation data fusion, which fuses the navigation data of all nodes and improve the motion information precision of all nodes. The dynamic vibration experiments show that the proposed deformation measurement method can achieve better measurement accuracy than fiber brag grating sensor, in which the deflection error is reduced by 20%, and the standard deviation of bending angle error is reduced by 55%. Based on the above deformation data, the innovative distributed filter can further reduce the position error obviously. Especially, the height error can be decreased by over 36%.

Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Innovative tactics are employed by terrorists to conceal weapons and explosives to perpetrate violent attacks, accounting for the deaths of millions of lives every year and contributing to huge economic losses to the global society. Achieving a high threat detection rate during an inspection of crowds to recognize and detect threat elements from a secure distance is the motivation for the development of intelligent image data analysis from a machine learning perspective. A method proposed to reduce the image dimensions with support vector, linearity and orthogonal. The functionality of CWD is contingent upon the plenary characterization of fusion data from multiple image sensors. The proposed method combines multiple sensors by hybrid fusion of sigmoidal Hadamard wavelet transform and PCA basis functions. Weapon recognition and the detection system, using Image segmentation and K means support vector machine A classifier is an autonomous process for the recognition of threat weapons regardless of make, variety, shape, or position on the suspect’s body despite concealment.

A single sensor based multispectral imaging camera using a narrow spectral band color mosaic integrated on the monochrome CMOS image sensor

A multispectral image camera captures image data within specific wavelength ranges in narrow wavelength bands across the electromagnetic spectrum. Images from a multispectral camera can extract a additional information that the human eye or a normal camera fails to capture and thus may have important applications in precision agriculture, forestry, medicine, and object identification. Conventional multispectral cameras are made up of multiple image sensors each fitted with a narrow passband wavelength filter and optics, which makes them heavy, bulky, power hungry, and very expensive. The multiple optics also create an image co-registration problem. Here, we demonstrate a single sensor based three band multispectral camera using a narrow spectral band red–green–blue color mosaic in a Bayer pattern integrated on a monochrome CMOS sensor. The narrow band color mosaic is made of a hybrid combination of plasmonic color filters and a heterostructured dielectric multilayer. The demonstrated camera technology has reduced cost, weight, size, and power by almost n times (where n is the number of bands) compared to a conventional multispectral camera.

Yield estimation in cotton using UAV-based multi-sensor imagery

Monitoring crop development and accurately estimating crop yield are important to improve field management and crop production. This study aimed to evaluate the performance of an unmanned aerial vehicle (UAV)-based remote sensing system in cotton yield estimation. A UAV system, equipped with an RGB camera, a multispectral camera, and an infrared thermal camera, was used to acquire images of a cotton field at two growth stages (flowering growth stage and shortly before harvest). Sequential images from the three cameras were processed to generate orthomosaic images and a digital surface model (DSM), which were registered to the georeferenced yield data acquired by a yield monitor mounted on a harvester. Eight image features were extracted, including normalised difference vegetation index ( NDVI ), green normalised difference vegetation index ( GNDVI ), triangular greenness index ( TGI ), a channel in CIE-LAB colour space ( a∗ ), canopy cover, plant height ( PH ), canopy temperature, and cotton fibre index ( CFI ). Models were developed to evaluate the accuracy of each image feature for yield estimation. Results show that PH and CFI were the best single features for cotton yield estimation, both with R 2 = 0.90. The combination of PH and CFI , PH and a ∗, or PH and temperature were the best two-feature models with R 2 from 0.92 to 0.94. The best three-feature models were among the combinations of PH , CFI , temperature and a ∗. This study found that UAV-based images collected during the flowering growth stage and/or shortly before harvest were able to estimate cotton yield accurately. • Site-specific cotton yield estimation was conducted. • The performance of multiple image sensors were evaluated. • Data were registered based on image features. • Methods were potentially used for precision crop management.

Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops.

The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6–2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops.

Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible-Light Images Captured by Smartphone Cameras

Automatic recognition of fake banknotes is an important task in practical banknote handling. Research on this task has mostly involved methods applied to automatic sorting machines with multiple imaging sensors or that use specialized sensors for capturing banknote images in various light wavelengths. These approaches can make use of the security features on banknotes for counterfeit detection. However, they require specialized devices, which are not always available for general users or visually impaired people. Meanwhile, smartphones are becoming more popular and can be useful imaging devices. Moreover, the types of fake banknotes created by imaging devices such as smartphone cameras or scanners are sometimes cannot be recognized by especially the visually impaired people. Addressing these problems, we propose a method for classifying fake and genuine banknotes using visible-light images captured by smartphone cameras based on convolutional neural networks. Experimental results on a self-collected dataset of US dollar, Euro, Korean won, and Jordanian dinar banknotes showed that our method performs better in terms of fake detection than the state-of-the-art methods.

Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition

Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation approach, named IrisParseNet . Different from many previous CNN-based iris segmentation methods, which only focus on predicting accurate iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging iris databases, i.e., CASIA.v4-distance, UBIRIS.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed iris segmentation method can be used for any iris recognition methodology, and would significantly improve the performance of non-cooperative iris recognition.

Surface Roughness-Induced Spectral Degradation of Multi-Spaceborne Solar Diffusers Due to Space Radiation Exposure

Solar diffusers (SDs) have often been used as the onboard calibrators for the radiometric calibration of reflective solar band imaging sensors. After being spaceborne, the reflectance of SDs is observed to degrade with spectral dependence due to exposure to solar UV and energetic particle radiation. Long-term spectral reflectance data of SDs onboard multiple LEO imaging sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) on SNPP, are analyzed. The reflectance of SDs on these three instruments degrades faster for the shorter wavelength (0.4– $0.6~\mu \text{m}$ ) bands than the longer wavelength bands. The Surface Roughness-induced Rayleigh Scattering (SRRS) model is applied to simulate the SD degradation on these instruments, and the growth of the surface roughness parameter of the SDs is derived. It is determined that the change of surface roughness scale length is ~tens of nanometers. To show the consistency of roughness growth rates among the SDs on Terra/Aqua MODIS and SNPP VIIRS instruments, the functional dependences of the growth rates are characterized according to the SD exposure time and the stage of surface roughness. It is also found that the flattening or reverse in the growth trend of the surface roughness for these three SDs occurred around the same interval between October 2013 and October 2015. The confirmation of the applicability of SRRS model with the long-term spectral reflectance data from three independent spaceborne SDs facilitates a better understanding of the origin and physical processes of the SD degradation.

Implementation and Evaluation of a Wide-Range Human-Sensing System Based on Cooperating Multiple Range Image Sensors.

A museum is an important place for science education for children. The learning method in the museum is reading exhibits and explanations. Museums are investing efforts to quantify interests using questionnaires and sensors to improve their exhibitions and explanations. Therefore, even in places where many people gather, such as in museums, it is necessary to quantify people’s interest by sensing behavior of multiple people. However, this has not yet been realized. We aim to quantify the interest by sensing a wide range of human behavior for multiple people by coordinating multiple noncontact sensors. When coordinating multiple sensors, the coordinates and the time of each sensor differ. To solve these problems, coordinates were transformed using a simultaneous transformation matrix and time synchronization was performed using unified time. The effectiveness of this proposal was verified through experimental evaluation. Furthermore, we evaluated the actual museum content. In this paper, we describe the proposed method and the results of the evaluation experiment.

Tutorial: Dealing with rotation matrices and translation vectors in image-based applications: A tutorial

Rotation matrices are a convenient and intuitive way to describe algebraically the relative orientation of multiple cameras or of the same camera shooting from different points of view. However, the definition of a rotation matrix is prone to intrinsic ambiguity, which often leads to a mismatch with the physical rotation one wants to describe, even if the definition is mathematically correct. This is a common source of errors whenever it is required to compute a rotation matrix from camera orientation data, or vice versa, to recover such data from a given rotation matrix. This tutorial aims to describe and solve the main factors that generate the ambiguity in using rotation matrices and to permit dealing with them properly both in theory and in practice. Through a detailed analysis of these factors, which ranges from basic mathematical aspects to the notation used to refer to them, it is shown how to avoid errors in the algebraic description of the relative orientation of different cameras by means of rotation matrices. This work is followed by another contribution, in which the interaction between rotation matrices and translation vectors (used to describe the shifts between pairs of cameras) is also analyzed, and a recommendation on how to define a common reference system coherent with a camera (a crucial aspect to model the camera acquisition geometry) is given. The two contributions jointly embrace the entire description of the relative acquisition geometry of images taken from different points of view and provide a complete and error-free methodology to recover it or to extract useful data from it. This topic is particularly important in a wide variety of aerospace applications, which often rely on multiple imaging sensors whose information should be merged, or on imaging devices carried by manned or unmanned vehicles. Such applications range from flying object detection to tridimensional reconstruction by using aerial or satellite images to drone automatic navigation, to change detection for area monitoring to georegistration by ground-to-aerial image matching.