A Systematic Solution to Multi-Instrument Coregistration of High-Resolution Planetary Images to an Orthorectified Baseline

We address the problem of automatically coregistering planetary images to a common baseline, introducing a novel generic technique that achieves an unprecedented robustness to different image inputs, thus making batch-mode coregistration achievable without requiring the usual parameter tweaking. We introduce a novel image matching technique, which boosts matching performance even under the most strenuous circumstances, and experimentally demonstrate validation through an extensive experimental multi-instrument setup that includes images from eight high-resolution data sets of the Mars and the Moon. The technique is further tested in a batch-mode processing, in which approximately 1.6% of all high-resolution Martian imagery is coregistered to a common baseline

An Improved Harris-SIFT Algorithm Based on Rotation-invariant LBP Operator

Feature-points matching is an important concept in binocular stereo vision. The procession of multi-scale feature-points matching in classical Harris-SIFT algorithm is time-consuming and has high complexity when describing the feature-points. This paper proposed a new improved Harris-SIFT algorithm based on rotation-invariant LBP (Local binary patterns) operator. Firstly, the Harris operator is used to extract feature points from DOG (Difference of Gaussian) scale space. Then, the dominant direction of feature point is calculated and 81-dimensional rotation-invariant LBP descriptors are extracted when the rotation matching window is coordinated to this direction. At last, Best-Bin-First (BBF) algorithm is used to search the matching points between the two sets of feature points. Experimental results show that the proposed algorithm is lower time-consuming than classical Harris-SIFT algorithm and remains the similar matching correct rate

3D modeling of ultra-high-resolution UAV imagery using low-cost photogrammetric software and structure from motion

The availability of advanced low-cost unmanned aerial systems (UASs), aftermarket applications, and a competitive market for processing software have provided researchers new opportunities for employing high resolution remote sensing in research. The UAS allows for the capture of close range aerial imagery that can then be used to generate dense point clouds using structure from motion (SfM). A variety of digital products can be created form these dense point clouds such as three-dimensional models, digital elevation models (DEMs), digital surface models (DSMs) and orthomosaics. This dissertation looks at methods and accuracies associated with the creation of digital mapping products from dense point clouds generated from imagery captured by two low-cost off the shelf UASs. The UASs were used to capture imagery over a 2-hectare vineyard in the Uwharrie mountains of North Carolina. Aspects of imagery collection, such as altitude, ground control, camera types, flight paths, and target styles, were investigated for their impacts on accuracy. Thirty-one ground control points were created in the vineyard using a survey grade GNSS receiver and total station for use in georeferencing. The number of ground control points used for georeferencing were reduced until a significant difference in accuracy was found using t-tests. Five ground control points were shown to be the least amount of ground control needed before accuracy began to change significantly. Four flight altitudes were tested with 80-meters generating the least level of error. Orthomosaics created from structure from motion and imagery collected using a global shutter 20-megapixel had total RMS errors between 2-4 cm

Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing

This PhD project develops methods for the assessment of forest condition utilising modern remote sensing technologies, in particular optical imagery from unmanned aerial systems and with Structure from Motion photogrammetry. The research focuses on health threats to the UK’s native oak trees, specifically, Chronic Oak Decline (COD) and Acute Oak Decline (AOD). The data requirements and methods to identify these complex diseases are investigatedusing RGB and multispectral imagery with very high spatial resolution, as well as crown textural information. These image data are produced photogrammetrically from multitemporal unmanned aerial vehicle (UAV) flights, collected during different seasons to assess the influence of phenology on the ability to detect oak decline. Particular attention is given to the identification of declined oak health within the context of semi-natural forests and heterogenous stands. Semi-natural forest environments pose challenges regarding naturally occurring variability. The studies investigate the potential and practical implications of UAV remote sensing approaches for detection of oak decline under these conditions. COD is studied at Speculation Cannop, a section in the Forest of Dean, dominated by 200-year-old oaks, where decline symptoms have been present for the last decade. Monks Wood, a semi-natural woodland in Cambridgeshire, is the study site for AOD, where trees exhibit active decline symptoms. Field surveys at these sites are designed and carried out to produce highly-accurate differential GNSS positional information of symptomatic and control oak trees. This allows the UAV data to be related to COD or AOD symptoms and the validation of model predictions. Random Forest modelling is used to determine the explanatory value of remote sensing-derived metrics to distinguish trees affected by COD or AOD from control trees. Spectral and textural variables are extracted from the remote sensing data using an object-based approach, adopting circular plots around crown centres at individual tree level. Furthermore, acquired UAV imagery is applied to generate a species distribution map, improving on the number of detectable species and spatial resolution from a previous classification using multispectral data from a piloted aircraft. In the production of the map, parameters relevant for classification accuracy, and identification of oak in particular, are assessed. The effect of plot size, sample size and data combinations are studied. With optimised parameters for species classification, the updated species map is subsequently employed to perform a wall-to-wall prediction of individual oak tree condition, evaluating the potential of a full inventory detection of declined health. UAV-acquired data showed potential for discrimination of control trees and declined trees, in the case of COD and AOD. The greatest potential for detecting declined oak condition was demonstrated with narrowband multispectral imagery. Broadband RGB imagery was determined to be unsuitable for a robust distinction between declined and control trees. The greatest explanatory power was found in remotely-sensed spectra related to photosynthetic activity, indicated by the high feature importance of nearinfrared spectra and the vegetation indices NDRE and NDVI. High feature importance was also produced by texture metrics, that describe structural variations within the crown. The findings indicate that the remotely sensed explanatory variables hold significant information regarding changes in leaf chemistry and crown morphology that relate to chlorosis, defoliation and dieback occurring in the course of the decline. In the case of COD, a distinction of symptomatic from control trees was achieved with 75 % accuracy. Models developed for AOD detection yielded AUC scores up to 0.98,when validated on independent sample data. Classification of oak presence was achieved with a User’s accuracy of 97 % and the produced species map generated 95 % overall accuracy across the eight species within the study area in the north-east of Monks Wood. Despite these encouraging results, it was shown that the generalisation of models is unfeasible at this stage and many challenges remain. A wall-to-wall prediction of decline status confirmed the inability to generalise, yielding unrealistic results, with a high number of declined trees predicted. Identified weaknesses of the developed models indicate complexity related to the natural variability of heterogenous forests combined with the diverse symptoms of oak decline. Specific to the presented studies, additional limitations were attributed to limited ground truth, consequent overfitting,the binary classification of oak health status and uncertainty in UAV-acquired reflectance values. Suggestions for future work are given and involve the extension of field sampling with a non-binary dependent variable to reflect the severity of oak decline induced stress. Further technical research on the quality and reliability of UAV remote sensing data is also required

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications