bluuebunny/crossref_metadata_2025 huggingface dataset:
A NOVEL METHOD FOR ESTIMATION OF STRUCTURAL CHANGES IN POTATO CROPS FROM UAV-BASED DIGITAL SURFACE MODELS
Authors: Angulo-Morales V. , Rodríguez-Galvis J. , Lizarazo-Salcedo I. , Gaona-García E.
Published: August 2020

Abstract. Climate change is arising challenges in potato crop production. More robust clones and cultivars adapted to climate change and resistant to pests and diseases, need to be developed. Several phenotyping approaches are being explored for a quick and affordable characterization of structural changes of crop varieties in real-world conditions. However, conventional methods present several problems. Field work in remote areas is time-consuming or difficult to carry out because of physical barriers. Satellite images lack the temporal resolution needed to monitor small changes in plant structure. Unmanned aerial vehicles (UAVs) provide high-resolution images which can give a better understanding of vegetation changes. Recent UAV-based studies have demonstrated the potential of the Normalized Difference Elevation Index (NDEI) to map centimeter-level changes in topography in different environments. This paper explores the potential of NDEI for identifying structural changes in experimental potato crops. In particular, it proposes an automatic method for estimation of volume changes in potato crop canopy. NDEI values were obtained by multi-temporal analysis of digital elevation models generated from UAV images using structure-from-motion (SfM) and multivision-stereo (MVS) techniques. The main components of the proposed method are: i) Image data capture from UAVs, ii) Data pre-processing, iii) NDEI calculation, and iv) Data processing and analysis. The results suggest that changes in surface elevation values, normalized using the NDEI index, are useful for rapid assessment of volume change in potato crop canopy. The NDEI reveals as an affordable technique to field measurement of phenotypic traits in potato breeding programs.
LEARNING MAPS FOR OBJECT LOCALIZATION USING VISUAL-INERTIAL ODOMETRY
Authors: Zha B. , Yilmaz A.
Published: August 2020

Abstract. Objects follow designated path on maps, such as vehicles travelling on a road. This observation signifies topological representation of objects’ motion on the map. Considering the position of object is unknown initially, as it traverses the map by moving and turning, the spatial uncertainty of its whereabouts reduces to a single location as the motion trajectory would fit only to a certain map trajectory. Inspired by this observation, we propose a novel end-to-end localization approach based on topological maps that exploits the object motion and learning the map using an recurrent neural network (RNN) model. The core of the proposed method is to learn potential motion patterns from the map and perform trajectory classification in the map’s edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and an RNN is trained from the map for each representation to compare their performances. The localization accuracy in the tested map for the angle and augmented angle representations are 90.43% and 96.22% respectively. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize objects based on their motion.
MULTI-AGENT PATH PLANNING OF ROBOTIC SWARMS IN AGRICULTURAL FIELDS
Authors: Botteghi N. , Kamilaris A. , Sinai L. , Sirmacek B.
Published: August 2020

Abstract. Collaborative swarms of robots/UAVs constitute a promising solution for precision agriculture and for automatizing agricultural processes. Since agricultural fields have complex topologies and different constraints, the problem of optimized path routing of these swarms is important to be tackled. Hence, this paper deals with the problem of optimizing path routing for a swarm of ground robots and UAVs in different popular topologies of agricultural fields. Four algorithms (Nearest Neighbour based on K-means clustering, Christofides, Ant Colony Optimisation and Bellman-Held-Karp) are applied on various farm types commonly found around Europe. The results indicate that the problem of path planning and the corresponding algorithm to use, are sensitive to the field topology and to the number of agents in the swarm.
A NEW METHOD TO EXTRACT PERMANENT SCATTERERS
Authors: Kang Y. , Zhang Y. , Wu H.
Published: August 2020

Abstract. At present, time series InSAR technology has been widely used in surface deformation monitoring. The extraction of permanent scatterers is an important part, which is directly related to the accuracy of monitoring results. The existing permanent scatterers extraction methods are mainly based on the amplitude information or the coherence information, there is a problem that the point quality and point density cannot be taken into account, and the selection of the point parameters requires operators to have a wealth of experience. In order to solve the above problems, a permanent scatterers extraction method based on the combination of amplitude and model coherence coefficient is proposed in this paper. This method examines not only the amplitude information of the permanent scatterers, but also the phase information of the point. And the phase quality directly affects the accuracy of deformation inversion. This paper takes Yupu Bridge, Yiqiao Town, Xiaoshan District, Hangzhou City as the experimental area to carry out comparative experiments. The results show that the final point target density extracted by this method is 1.97 times that of the conventional method based on amplitude information, and shows the details of deformation distribution of Yupu Bridge more completely.
GEOMETRY-BASED REGULARISATION FOR DENSE IMAGE MATCHING VIA UNCERTAINTY-DRIVEN DEPTH PROPAGATION
Authors: Höllmann M. , Mehltretter M. , Heipke C.
Published: August 2020

Abstract. In the present work, an uncertainty-driven geometry-based regularisation for the task of dense stereo matching is presented. The objective of the regularisation is the reduction of ambiguities in the depth reconstruction process, which exist due to the ill-posed nature of this task. Based on cost and uncertainty information computed beforehand, pixels are selected, whose depth information can be determined correctly with a high probability. This depth information assumed to be of high confidence is initially used to construct a triangle mesh, which is interpreted as surface approximation of the imaged scene and allows to propagate the confident depth information of the triangle vertices within local neighbourhoods. The proposed method further computes confidence scores for propagated depth estimates, which are used to fuse this depth information with the previously computed cost information, introducing a regularisation into the data term of global optimisation methods. Furthermore, based on the propagated depth information the local smoothness assumption of global optimisation methods is adjusted. Instead of fronto-parallel planes, the method presumes planes, which are parallel to the propagated depth information. The performance of the proposed regularisation approach is evaluated in combination with a global optimisation method. For a quantitative and qualitative evaluation two commonly employed and well-established stereo datasets are used. The proposed method shows significant improvements in accuracy on both datasets and for two different cost computation methods. Especially in unstructured areas, artefacts in the disparity maps are reduced.
HIERARCHICAL PROXIMITY-BASED OVER-SEGMENTATION OF 3-D POINT CLOUDS FOR EFFICIENT GRAPH FEATURE DETECTION
Authors: Shtain Z. , Filin S.
Published: August 2020

Abstract. Point cloud simplification is empowered by the definition of similarity metrics which we aim to identify homogeneous regions within the point-cloud. Nonetheless, the variety of shapes and clutter in natural scenes, along with the significant resolution variations, occlusions, and noise, contribute to inconsistencies in the geometric properties, thereby making the homogeneity measurement challenging. Thus, the objective of this paper is to develop a point-cloud simplification model by means of data segmentation and to extract information in a better-suited way. The literature shows that most approaches either apply volumetric data strategies and/or resort to simplified planar geometries, which relate to only part of the entities found within a natural scene. To provide a more general strategy, we propose a proximity-based approach that allows an efficient and reliable surface characterization with no limitation on the number or shape of the primitives which in turn, enables detecting free-form objects. To achieve this, a local, computationally efficient and scalable metric is developed, which captures resolution variation and allows for short processing time. Our proposed scheme is demonstrated on datasets featuring a variety of surface types and characteristics. Experiments show high precision rates while exhibiting robustness to the varying resolution, texture, and occlusions that exist within the sets.
ON SELECTING IMAGES FROM AN UNAIMED VIDEO STREAM FOR PHOTOGRAMMETRIC MODELLING
Authors: Rönnholm P. , Vaaja M. T. , Kauhanen H. , Klockars T.
Published: August 2020

Abstract. In this paper, we illustrate how convolutional neural networks and voxel-based processing together with voxel visualizations can be utilized for the selection of unaimed images for a photogrammetric image block. Our research included the detection of an ear from images with a convolutional neural network, computation of image orientations with a structure-from-motion algorithm, visualization of camera locations in a voxel representation to detect the goodness of the imaging geometry, rejection of unnecessary images with an XYZ buffer, the creation of 3D models in two different example cases, and the comparison of resulting 3D models. Two test data sets were taken of an ear with the video recorder of a mobile phone. In the first test case, a special emphasis was taken to ensure good imaging geometry. On the contrary, in the second test case the trajectory was limited to approximately horizontal movement, leading to poor imaging geometry. A convolutional neural network together with an XYZ buffer managed to select a useful set of images for the photogrammetric 3D measuring phase. The voxel representation well illustrated the imaging geometry and has potential for early detection where data is suitable for photogrammetric modelling. The comparison of 3D models revealed that the model from poor imaging geometry was noisy and flattened. The results emphasize the importance of good imaging geometry.
MONITORING MOUNTAIN CRYOSPHERE DYNAMICS BY TIME LAPSE STEREO PHOTOGRAMMETRY
Authors: Marsy G. , Vernier F. , Bodin X. , Cusicanqui D. , Castaings W. , Trouvé E.
Published: August 2020

Abstract. In this paper, we propose a method to monitor surface dynamics in mountain cryosphere environments, based on a device with two fixed cameras at two different locations and with a convergent angle. Computer vision methods are used to derive pixel displacements between images taken at different times and to reconstruct the 3D geometry of the scene to convert pixel displacements into meter displacements. The proposed methods overcome the drawbacks of traditional methods such as lack of time repeatability or lack of spatial resolution. We present the results on two study sites instrumented with the device located in the French Alps: a temperate glacier and a rock glacier. We propose different ways to assess the accuracy of each processing steps solely based on stereo images. The method is validated using traditional measurements (GPS and LiDAR) and shows results comparable or even more robust than these traditional methods.
ADAPTABLE AUTOREGRESSIVE MOVING AVERAGE FILTER TRIGGERING CONVOLUTIONAL NEURAL NETWORKS FOR CHOREOGRAPHIC MODELING
Authors: Rallis I. , Bakalos N. , Doulamis N. , Doulamis A.
Published: August 2020

Abstract. Choreographic modeling, that is identification of key choreographic primitives, is a significant element for Intangible Cultural Heritage (ICH) performing art modeling. Recently, deep learning architectures, such as LSTM and CNN, have been utilized for choreographic identification and modeling. However, such approaches present sensitivity to capturing errors and fail to model the dynamic characteristics of a dance, since they assume a stationarity between the input-output data. To address these limitations, in this paper, we introduce an AutoRegressive Moving Average (ARMA) filter into a conventional CNN model; this means that the classification output feeds back to the input layer, improving overall classification accuracy. In addition, an adaptive implementation algorithm is introduced, exploiting a first-order Taylor series expansion, to update network response in order to fit dance dynamic characteristics. This way, the network parameters (e.g., weights) are dynamically modified improving overall classification accuracy. Experimental results on real-life dance sequences indicate the out-performance of the proposed approach with respect to conventional deep learning mechanisms.
BUNDLE BLOCK ADJUSTMENT WITH CONSTRAINED RELATIVE ORIENTATIONS
Authors: Maset E. , Magri L. , Toschi I. , Fusiello A.
Published: August 2020

Abstract. This paper deals with bundle adjustment with constrained cameras, i.e. where the orientation of certain cameras is expressed relatively to others, and these relative orientations are part of the unknowns. Despite the remarkable interest for oblique multi-camera systems, an empirical study on the effect of enforcing relative orientation constraints in bundle adjustment is still missing. We provide experimental evidence that indeed these constraints improve the accuracy of the results, while reducing the computational load as well. Moreover, we report for the first time in the literature the complete derivation of the Jacobian matrix for bundle adjustment with constrained cameras, to foster other implementations.
HYBRID ACQUISITION OF HIGH QUALITY TRAINING DATA FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUDS USING CROWD-BASED ACTIVE LEARNING
Authors: Kölle M. , Walter V. , Schmohl S. , Soergel U.
Published: August 2020

Abstract. Automated semantic interpretation of 3D point clouds is crucial for many tasks in the domain of geospatial data analysis. For this purpose, labeled training data is required, which has often to be provided manually by experts. One approach to minimize effort in terms of costs of human interaction is Active Learning (AL). The aim is to process only the subset of an unlabeled dataset that is particularly helpful with respect to class separation. Here a machine identifies informative instances which are then labeled by humans, thereby increasing the performance of the machine. In order to completely avoid involvement of an expert, this time-consuming annotation can be resolved via crowdsourcing. Therefore, we propose an approach combining AL with paid crowdsourcing. Although incorporating human interaction, our method can run fully automatically, so that only an unlabeled dataset and a fixed financial budget for the payment of the crowdworkers need to be provided. We conduct multiple iteration steps of the AL process on the ISPRS Vaihingen 3D Semantic Labeling benchmark dataset (V3D) and especially evaluate the performance of the crowd when labeling 3D points. We prove our concept by using labels derived from our crowd-based AL method for classifying the test dataset. The analysis outlines that by labeling only 0:4% of the training dataset by the crowd and spending less than 145 $, both our trained Random Forest and sparse 3D CNN classifier differ in Overall Accuracy by less than 3 percentage points compared to the same classifiers trained on the complete V3D training set.
ON THE ASSOCIATION OF LIDAR POINT CLOUDS AND TEXTURED MESHES FOR MULTI-MODAL SEMANTIC SEGMENTATION
Authors: Laupheimer D. , Shams Eddin M. H. , Haala N.
Published: August 2020

Abstract. The semantic segmentation of the huge amount of acquired 3D data has become an important task in recent years. We propose a novel association mechanism that enables information transfer between two 3D representations: point clouds and meshes. The association mechanism can be used in a two-fold manner: (i) feature transfer to stabilize semantic segmentation of one representation with features from the other representation and (ii) label transfer to achieve the semantic annotation of both representations. We claim that point clouds are an intermediate product whereas meshes are a final user product that jointly provides geometrical and textural information. For this reason, we opt for semantic mesh segmentation in the first place. We apply an off-the-shelf PointNet++ to a textured urban triangle mesh as generated from LiDAR and oblique imagery. For each face within a mesh, a feature vector is computed and optionally extended by inherent LiDAR features as provided by the sensor (e.g. intensity). The feature vector extension is accomplished with the proposed association mechanism. By these means, we leverage inherent features from both data representations for the semantic mesh segmentation (multi-modality). We achieve an overall accuracy of 86:40% on the face-level on a dedicated test mesh. Neglecting LiDAR-inherent features in the per-face feature vectors decreases mean intersection over union by ∼2%. Leveraging our association mechanism, we transfer predicted mesh labels to the LiDAR point cloud at a stroke. To this end, we semantically segment the point cloud by implicit usage of geometric and textural mesh features. The semantic point cloud segmentation achieves an overall accuracy close to 84% on the point-level for both feature vector compositions.
INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS
Authors: Tilon S. M. , Nex F. , Duarte D. , Kerle N. , Vosselman G.
Published: August 2020

Abstract. Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.
DIAMONDNET: SHIP DETECTION IN REMOTE SENSING IMAGES BY EXTRACTING AND CLUSTERING KEYPOINTS IN A DIAMOND
Authors: Zhu Z. , Diao W. , Chen K. , Zhao L. , Yan Z. , Zhang W. , Xu G. , Sun X.
Published: August 2020

Abstract. Ship detection plays an important role in military and civil fields. Despite it has been studied for decays, ship detection in remote sensing images is still a challenging topic. In this work, we come up with a novel ship detection framework based on the keypoint extraction technique. We use a convolutional neural network to detect ship keypoints and then cluster the keypoints into groups, where each group is composed of keypoints belonging to the same ship. The choice of the keypoints is specifically considered to derive an effective ship representation. One keypoint is located at the center of the ship and the rest four keypoints are located at the head, the tail, the midpoint of the left side and the midpoint of the right side, respectively. Since these keypoints are distributed in a diamond, we name our network DiamondNet. In addition, a corresponding clustering algorithm based on the geometric characteristics of the ships is proposed to cluster keypoints into groups. We demonstrate that our method provides a more flexible and effective way to represent ships than the popular anchor-based methods, since either the rectangular bounding box or the rotated bounding box of each ship instance can be easily derived from the ship keypoints. Experiments on two datasets reveal that our DiamondNet reaches the state-of-the-art results.
“WEBSNOW”: ESTIMATION OF SNOW COVER FROM FREELY ACCESSIBLE WEBCAM IMAGES IN THE ALPS
Authors: Flöry S. , Ressl C. , Hollaus M. , Pürcher G. , Piermattei L. , Pfeifer N.
Published: August 2020

Abstract. The alpine snow cover exhibits a high spatial variability in the horizontal and vertical directions even on a very small scale, mainly caused by the high variability of alpine terrain. To quantify the annual and inter-annual snow dynamics continuously reliable measurements of the temporal and spatial variability are required. While remote sensing from satellite and aerial platforms have been successfully used to estimate snow cover at larger scales, especially in mountain areas spatial and temporal resolution are too low to capture local changes. In the alpine region, webcam images are freely available for touristic purposes capturing images at high frequency intervals. Within the WebSnow project the feasibility of using such images for the detection of snow was investigated. With the developed workflow, processing times could be reduced and satisfactory results obtained. Our results show, that webcam networks have the potential for monitoring snow at high spatial and temporal resolution.
CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS
Authors: Gehrung J. , Hebel M. , Arens M. , Stilla U.
Published: August 2020

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.
TOWARDS STRUCTURELESS BUNDLE ADJUSTMENT WITH TWO- AND THREE-VIEW STRUCTURE APPROXIMATION
Authors: Rupnik E. , Pierrot Deseilligny M.
Published: August 2020

Abstract. The global approaches solve SfM problems by independently inferring relative motions, followed be a sequential estimation of global rotations and translations. It is a fast approach but not optimal because it relies only on pairs and triplets of images and it is not a joint optimisation. In this publication we present a methodology that increases the quality of global solutions without the usual computational burden tied to the bundle adjustment. We propose an efficient structure approximation approach that relies on relative motions known upfront. Using the approximated structure, we are capable of refining the initial poses at very low computational cost. Compared to different benchmark datasets and software solutions, our approach improves the processing times while maintaining good accuracy.
STRUCTURE-FROM-MOTION PROCESSING OF AERIAL ARCHIVE PHOTOGRAPHS: SENSITIVITY ANALYSES PAVE THE WAY FOR QUANTIFYING GEOMORPHOLOGICAL CHANGES SINCE 1978 IN LA RÉUNION ISLAND
Authors: Rault C. , Dewez T. J. B. , Aunay B.
Published: August 2020

Abstract. Landscapes have been photographed dozens of times at scales ca. 1/25,000 and better since World War II. Scans are distributed freely online (e.g. remonterletemps.ign.fr). In parallel, Structure-from-Motion (SFM) software made photogrammetric processing easy to non-specialists. Yet puzzling questions crop up to use both: (i) Can raw scans be used as is? (ii) Can Ground Control Points (GCP) and checkpoints be safely collected from a web portal? (iii) How many parameters are sufficient for camera interior orientation? (iv) Are single flight camera networks sufficient to constrain camera models compared to multiple flights? (v) Are photogrammetric Digital Surface Models (DSM) fit for quantifying landslide activity? Processing of scanned black-and-white 1/27,000 photographs from IGN flown in May 1978 over Cirque de Salazie in La Réunion Island answer these questions. We find that raw scanned photographs need translation, rotation and cropping to match the camera reference frame. GCP and Check point coordinates collected on geoportail.gouv.fr with assumed accuracy of 10 m, achieved ca. 7 m accurate SFM registration. The optimal camera model uses only 4 parameters: f, cx, cy and K1. Compared to a 2015 lidar Digital Terrain Model (DTM), the 0.66 m/pixel DSM of 1978 has a median deviation of −1.39 m ± 3.34 m (Median Absolute Deviation) which is comparable to GCP quality. Elevation difference more importantly reveals, for the first time, the 37 years and 13 cyclones cumulated landslides pattern on Cirque de Salazie. Photographic archives hold decades-long 3D history. SFM is a game changer for landslide risk mitigation planning.
INFLUENCE OF RANGING UNCERTAINTY OF TERRESTRIAL LASER SCANNING ON CHANGE DETECTION IN TOPOGRAPHIC 3D POINT CLOUDS
Authors: Winiwarter L. , Anders K. , Wujanz D. , Höfle B.
Published: August 2020

Abstract. Terrestrial laser scanners are commonly used for remotely sensing natural surfaces into 3D point clouds. Time series of such 3D point clouds can be analysed to gain information of surface changes that are induced by Earth surface shaping processes. The atomic unit in time series analysis is a bitemporal change detection and quantification. This should involve an estimation of the minimum quantifiable change, the Level of Detection, to separate signal from noise, e.g. stemming from the measurement. To enable such an estimation through error propagation, a model of the sensing instrument’s measurement uncertainty is required. In this work, we present an investigation on the ranging component of terrestrial laser scanning on this uncertainty and its influence on 3D distances between point clouds of two epochs. Specifically, we analyse the effects of incidence angle, intensity and range for different object materials, and make additional considerations with respect to waveform information returned by the sensor. We estimate a model for the rangefinder uncertainty of a terrestrial laser scanner and apply it on experimental data. The results show that using a sensor-specific model of ranging uncertainty allows an appropriate estimation of the Level of Detection. At a range of 60 m and a rotational displacement of 10°, this Level of Detection ranges between 0.1 mm to 1 mm for a white and a grey surface and up to 5 mm for a black surface. The completeness of the detection of significant change ranges from 60.2 % (black) to 89.8 % (grey) for the proposed method and from 65.5 % to 88.9 % for the baseline, when compared to tachymeter measurements. The similarity between the results is expected and suggests the validity of error propagation for the derivation of the Level of Detection.
ACCURATE REGISTRATION OF AERIAL IMAGES AND ALS-POINTCLOUD VIA AUTOMATED JUNCTION MATCHING AND PLANAR CONSTRAINTS
Authors: Wan Y. , Zhang Y. , Wang G. , Liu X.
Published: August 2020

Abstract. Accurate geometric registration of images and pointclouds is the key step of many 3D-reconstruction or 3D-sensing tasks. In this paper, a novel L-junction based approach is proposed for semi-automatic accurate registration of aerial images and the airborne laser scanning (ALS) point-cloud in urban areas. The approach achieves accurate registration by associating the LiDAR points with the local planes extracted via L-junction detection and matching from multi-view aerial images. An L-junction is an intersection of two line-segments. Through the forward intersection of multi-view corresponding L-junctions, an accurate local junction-plane can be obtained. In the proposed approach, L-junction is manually collected from one view on the flat object-surfaces like walls, roads, and roofs and then automatically matched to other views with the aid of epipolar-geometry and vanishing-point constraints. Then, a plane-constrained bundle block adjustment of the image-orientation parameters is conducted, where the LiDAR points are treated as reference data. The proposed approach was tested with two datasets collected in Guangzhou city and Ningbo city of China. The experimental results showed that the proposed approach had better accuracy than the closest-point based method. The horizontal/vertical registration RMS of the proposed approach reached 4.21cm/5.72cm in Guangzhou dataset and 4.46cm/4.34cm in Ningbo dataset, which was much less than the average LiDAR-point distance (over 25cm in both datasets) and was very close to the image GSDs (3.2cm in Guangzhou and 4.8cm in Ningbo) and the a-priori ranging accuracy of the ALS equipment (about 3cm).
PARKING OCCUPANCY ESTIMATION ON SENTINEL-1 IMAGES
Authors: Drouyer S. , de Franchis C.
Published: August 2020

Abstract. This paper presents a method to estimate the occupancy ratio of parkings from SAR satellite images. The algorithm takes as input a series of Sentinel-1 images along with a mask indicating where the parking is located and returns for each image an occupancy ratio. The method is generic and can easily be extended. We validate our results in two parts. First, we have created a dataset of Sentinel-1 GRD image time series where each image is associated to a ground truth parking occupancy ratio. This ground truth is estimated thanks to a surveillance camera that permanently films and records the parking. We observe a strong correlation between the estimated occupancy rate and the ground truth occupancy rate. Secondly, we estimate the occupancy ratio of the 250 largest retail parkings in France from January 2018 to April 2020. We observe that weekly and seasonal patterns are consistent with consumer and economic trends. Parking occupancy estimations also plummet during the COVID-19 containment measures.
COMPLETION OF SPARSE AND PARTIAL POINT CLOUDS OF VEHICLES USING A NOVEL END-TO-END NETWORK
Authors: Xia Y. , Liu W. , Luo Z. , Xu Y. , Stilla U.
Published: August 2020

Abstract. Completing the 3D shape of vehicles from real scan data, which aims to estimate the complete geometry of vehicles from partial inputs, acts as a role in the field of remote sensing and autonomous driving. With the recent popularity of deep learning, plenty of data-driven methods have been proposed. However, most of them usually require additional information as prior knowledge for the input, for example, semantic labels and symmetry assumptions. In this paper, we design a novel and end-to-end network, termed as S2U-Net, to achieve the completion of 3D shapes of vehicles from the partial and sparse point clouds. Our network includes two modules of the encoder and the generator. The encoder is designed to extract the global feature of the incomplete and sparse point cloud while the generator is designed to produce fine-grained and dense completion. Specially, we adopt an upsampling strategy to output a more uniform point cloud. Experimental results in the KITTI dataset illustrate our method achieves better performance than the state-of-arts in terms of distribution uniformity and completion quality. Specifically, we improve the translation accuracy by 50.8% and rotation accuracy by 40.6% evaluating completed results with a point cloud registration task.
ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING
Authors: Sauzède R. , Johnson J. E. , Claustre H. , Camps-Valls G. , Ruescas A. B.
Published: August 2020

Abstract. Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. bbp, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of bbp, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the bbp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.
TECHNIQUES FOR MOSAICS DOCUMENTATION THROUGH PHOTOGRAMMETRY DATA ACQUISITION. THE BYZANTINE MOSAICS OF THE NATIVITY CHURCH
Authors: Doria E. , Picchio F.
Published: August 2020

Abstract. This paper describes a sequence of actions developed to guarantee a reliable and suitable dataset for the creation of detailed ortho images of Nativity Church mosaics. During acquisition campaigns, different photogrammetric techniques were tested, and different survey instruments were compared to improve the quality of the data obtained. The different outputs allow the adjustment of the instrument parameters and the acquisition methods, to structure a methodological process aimed at obtaining an accurate level of detail to describe the individual mosaic tile.From the realization of a reliable photomosaics an automatic vectorization system has been developed. This process, aimed at digitizing the tiles of the Church walls and the pavement mosaics, responds to a documentation and management purpose and to an objective of structuring a data acquisition method and post-production that can be replicated on other mosaic contexts.
WIDEBAND SATCOM MODEL: EVALUATION OF NUMERICAL ACCURACY AND EFFICIENCY
Authors: Knisely A. J. , Terzuoli A. J.
Published: August 2020

Abstract. The spectral method is typically applied as a simple and efficient method to solve the parabolic wave equation in phase screen scintillation models. The critical factors that can greatly affect the spectral method accuracy is the uniformity and smoothness of the input function. This paper observes these effects on the accuracy of the finite difference and the spectral methods applied to a wideband SATCOM signal propagation model simulated in the ultra-high frequency (UHF) band. The finite difference method uses local pointwise approximations to calculate a derivative. The spectral method uses global trigonometric interpolants that achieve remarkable accuracy for continuously differentiable functions. The differences in accuracy are presented for a Gaussian lens and Kolmogorov phase screen. The results demonstrate loss of accuracy in each method when a phase screen is applied, despite the spectral method's computational efficiency over the finite difference method. These results provide meaningful insights when discretizing an interior domain and solving the parabolic wave equation to obtain amplitude and phase of a signal perturbation.
CORRELATION ANALYSIS FOR TROPICAL FOREST ABOVE GROUND BIOMASS AND VERTICAL REFLECTIVITY PROFILE
Authors: Li W. , Wu J. , Liu L. , Li Y. , Chen E.
Published: August 2020

Abstract. Tropical forests play a key role in the carbon cycle and sustainable development and their above ground biomass (AGB) is applied as an quantitative parameter for the research on global carbon cycle and ecological function. Ten-baseline Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) images collected in Mondah, Gabon 4th Feb. 2016 during AfriSAR campaign are applied to extract the vertical scattering coefficient with SAR tomography (TomoSAR). Capon is used to reconstruct the vertical reflectivity profile and then the correlation analysis between backscattering coefficients at special height and the in-situ F-AGB is carried out. The results show that Capon performs well when the perpendicular baseline interval is similar and the vertical reflectivity relates closely with the F-AGB in plot scale.
A NEW SPECTRAL-SPATIAL SUBSPACE CLUSTERING ALGORITHM FOR HYPERSPECTRAL IMAGE ANALYSIS
Authors: Rafiezadeh Shahi K. , Ghamisi P. , Jackisch R. , Khodadadzadeh M. , Lorenz S. , Gloaguen R.
Published: August 2020

Abstract. In the past decade, hyperspectral imaging techniques have been widely used in various applications to acquire high spectral-spatial resolution images from different objects and materials. Although hyperspectral images (HSIs) are useful tools to obtain valuable information from different materials, the processing of such data is challenging due to several reasons such as the high dimensionality and redundancy of the feature space. Therefore, advanced machine learning algorithms have been developed to analyse HSIs. Among the developed algorithms, unsupervised learning techniques have become popular since they are capable of processing HSIs without having prior knowledge. Generally, unsupervised learning algorithms analyse HSIs based on spectral information. However, in many applications, spatial information plays an eminent role, in particular when the input data is of high spatial resolution. In this study, we propose a new clustering approach by utilizing the sparse subspace-based concept within the hidden Markov random field (HMRF) structure to process HSIs in an unsupervised manner. The qualitative analyses of the obtained clustering results show that the proposed spectral-spatial clustering algorithm outperforms the sparse subspace-based clustering algorithm that only uses spectral information.
ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA
Authors: Kwan M. H. , Yan W. Y.
Published: August 2020

Abstract. Range normalization is a common data pre-process that aims to improve the radiometric quality of airborne LiDAR data. This radiometric treatment considers the rate of energy attenuation sustained by the laser pulse as it travels through a medium back and forth from the LiDAR system to the surveyed object. As a result, the range normalized intensity is proportional to the range to the power of a factor a. Existing literature recommended different a values on different land cover types, which are commonly adopted in forestry studies. Nevertheless, there is a lack of study evaluating the range normalization on multispectral airborne LiDAR intensity data. In this paper, we propose an overlap-driven approach that is able to estimate the optimal a value by pairing up the closest data points of two overlapping LiDAR data strips, and subsequently estimating the range normalization parameter a based on a least-squares adjustment. We implemented the proposed method on a set of multispectral airborne LiDAR data collected by a Optech Titan, and assessed the coefficient of variation of four land cover types before and after applying the proposed range normalization. The results showed that the proposed method was able to estimate the optimal a value, yielding the lowest cv, as verified by a cross validation approach. Nevertheless, the estimated a value is never identical for the four land cover classes and the three laser wavelengths. Therefore, it is not recommended to label a specific a value for the range normalization of airborne LiDAR intensity data within a specific land cover type. Instead, the range normalization parameter is deemed to be data-driven and should be estimated for each LiDAR dataset and study area.
GEOMETRIC AND NON-LINEAR RADIOMETRIC DISTORTION ROBUST MULTIMODAL IMAGE MATCHING VIA EXPLOITING DEEP FEATURE MAPS
Authors: Chen M. , Zhao Y. , Fang T. , Zhu Q. , Yan S. , Gao F.
Published: August 2020

Abstract. Image matching is a fundamental issue of multimodal images fusion. Most of recent researches only focus on the non-linear radiometric distortion on coarsely registered multimodal images. The global geometric distortion between images should be eliminated based on prior information (e.g. direct geo-referencing information and ground sample distance) before using these methods to find correspondences. However, the prior information is not always available or accurate enough. In this case, users have to select some ground control points manually to do image registration and make the methods work. Otherwise, these methods will fail. To overcome this problem, we propose a robust deep learning-based multimodal image matching method that can deal with geometric and non-linear radiometric distortion simultaneously by exploiting deep feature maps. It is observed in our study that some of the deep feature maps have similar grayscale distribution and correspondences can be found from these maps using traditional geometric distortion robust matching methods even significant non-linear radiometric difference exists between the original images. Therefore, we can only focus on the geometric distortion when we deal with deep feature maps, and then only focus on non-linear radiometric distortion in patches similarity measurement. The experimental results demonstrate that the proposed method performs better than the state-of-the-art matching methods on multimodal images with both geometric and non-linear radiometric distortion.
LAND SALINIZATION DYNAMICS BASED ON FEATURE SPACE COMBINATIONS FROM LANDSAT IMAGE IN TONGYU COUNTY, NORTHEAST CHINA
Authors: Huang F. , Wang P. , Liu Y. , Li H. Y.
Published: August 2020

Abstract. Land salinization is one of the most common land degradation processes. Tongyu county exemplifies all the forms of land degradation in Northeast China and is prone to land salinization due to its fragile physical conditions. In this study, Landsat remote sensing images are adopted to invert surface albedo, MSAVI (modified soil adjusted vegetation index), and salinity index (SI) data. The feature space models of albedo-SI and MSAVI-SI, considering the bare soil and vegetation information respectively, are constructed and compared. Land salinization changes for the period of 1998–2017 are investigated using the salinization monitoring index (SMI) and salinization detection index (SDI) extracted from the feature space models. Our results show that the land salinization situation in Tongyu county have tended to improve, associated with the biological, ecological and engineering means for the degraded land rehabilitation. The feature space models is applicable for the extraction of salinization information, and albedo-SI may evaluate land salinization levels with higher accuracy.
TESTING A COMBINED MULTISPECTRAL-MULTITEMPORAL APPROACH FOR GETTING CLOUDLESS IMAGERY FOR SENTINEL-2
Authors: Colaninno N. , Marambio A. , Roca J.
Published: August 2020

Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm suitable for optical imagery only. The approach combines a multispectral-multitemporal strategy to retrieve daytime cloudless and shadow-free imagery. While the approach has been explored for Landsat information, namely Landsat 5 TM and Landsat 8 OLI, here we aim at testing the suitability of the method for Sentinel-2 Multi-Spectral Instrument. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over a temporal period of few months. Besides, in order to emphasize the effectiveness of optical imagery for monitoring post-disaster events, two temporal stages have been processed, before and after a critical seismic event occurred in Lombok Island, Indonesia, in summer 2018. The approach relies on a clouds and cloud shadows masking algorithm, based on spectral features, and a data reconstruction phase based on automatic selection of the most suitable pixels from a multitemporal stack. Results have been tested with uncontaminated image samples for the same scene. High accuracy is achieved.
A NEW INDEX FOR IDENTIFYING WATER BODY FROM SENTINEL-2 SATELLITE REMOTE SENSING IMAGERY
Authors: Jiang W. , Ni Y. , Pang Z. , He G. , Fu J. , Lu J. , Yang K. , Long T. , Lei T.
Published: August 2020

Abstract. Water body plays an irreplaceable role in the global ecosystem and climate system. Sentinel-2 is a new satellite data with higher spatial and spectral resolution. Through analysing spectral characteristics of Sentinel-2 satellite imagery, the brightness of water body in vegetation red edge band and shortwave infrared band showe sharply different than that of the not water body. Therefore, a new type of water index SWI (Sentinel-2 Water Index) was proposed by combing those two bands. Four representative water types, which included Taihu Lake, the Yangtze River Estuary, the ChaKa Salt Lake and the Chain Lake, were selected as experimental areas. Normalized difference water index (NDWI) and Sentinel-2 Water Index (SWI) with Otsu method were employed to extract water body. The results showed that overall accuracy and Kappa coefficient of SWI were higher than that of NDWI and SWI was efficient index to rapidly and accurately extract water for Sentinel-2 data. Therefore, SWI had application potential for larger scale water mapping in the future.