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In recent years, the demand for inexpensive, simple, and highly accurate 3D measurement has been increasing. Representative methods, photogrammetry, and shape from focus (SfF) have limitations in terms of measurement time and labour. In order to solve them, computational photography (CP) has been proposed. A light field camera, based on CP, has also been developed. It has a feature to acquire multi-view and multi-focus images simultaneously in one shot. It is possible to perform 3D measurements with less time and labour for photographing and calculation processing using these images. In this study, we combined the photogrammetry as applied to multi-view images with the SfF as applied to multi-focus images using a light field camera. We applied the proposed method to a rigid body and verified its accuracy. We confirmed that the proposed method achieved more accurate results than the photogrammetry and the SfF method. Furthermore, we applied the proposed method to screws and cracks on walls of buildings and affirmed its applicability. Finally, we suggested future work on the developed method. [ABSTRACT FROM AUTHOR]

Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy. [ABSTRACT FROM AUTHOR]

Structure from motion (SfM) has been widely used to achieve automatic 3D reconstructions. However, as the 3D point clouds obtained via SfM are sparse, multi-view stereo (MVS) was developed to compensate for this sparseness. The accuracy of the 3D surface depends on the accuracy of the orientation elements based on the SfM. Additionally, in the case of an unmanned aerial vehicle (UAV), SfM exhibits a decrease in the accuracy of the orientation elements during complex camera movements. This paper proposes a patch-based MVS (PMVS) method considering the accuracy of the orientation elements. The proposed method involves applying the global SfM, estimating accuracy of exterior orientation (EO) elements, and introducing the accuracy of EO elements to PMVS. The PMVS approximates an object surface by using small rectangular patches, namely local tangent plane approximation. The patches are optimized by minimizing the sum of the photometric discrepancy scores. The accuracy of the EO elements is introduced to the patch optimization as weighting function. This accuracy is defined using the variances of the estimated parameters in the bundle adjustment. We also investigate the types of weighting functions. The results indicate that the proposed method is capable of considering geometric conditions during patch estimation. The proposed method was applied to the three types of image datasets, i.e., images captured using an SLR camera at ground level, images captured using a UAV equipped with a SLR camera, and images captured using an airplane equipped with an oblique camera. Through the experimental results, the improved accuracy and the effectiveness of the proposed method were confirmed. [ABSTRACT FROM AUTHOR]

Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING) method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images. [ABSTRACT FROM AUTHOR]

3D models have been widely used by spread of many available free-software. Additionally, enormous images can be easily acquired, and images are utilized for creating the 3D models recently. The creation of 3D models by using huge amount of images, however, takes a lot of time and effort, and then efficiency for 3D measurement are required. In the efficient strategy, the accuracy of the measurement is also required. This paper develops an image selection method based on network design that means surveying network construction. The proposed method uses image connectivity graph. The image connectivity graph consists of nodes and edges. The nodes correspond to images to be used. The edges connected between nodes represent image relationships with costs as accuracies of orientation elements. For the efficiency, the image connectivity graph should be constructed with smaller number of edges. Once the image connectivity graph is built, the image selection problem is regarded as combinatorial optimization problem and the graph cuts technique can be applied. In the process of 3D reconstruction, low quality images and similar images are also extracted and removed. Through the experiments, the significance of the proposed method is confirmed. It implies potential to efficient and accurate 3D measurement. [ABSTRACT FROM AUTHOR]

We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions simultaneously. PHD filter overcomes some limitations of conventional Gaussian decomposition method; PHD filter doesn't require a priori knowledge on the number of targets, assumption of parametric form of the intensity distribution. In addition, it can take a similarity between successive irradiations into account by modelling relative positions of the same targets spatially. Firstly we explain PHD filter and particle filter implementation to it. Secondly we formulate the multi-target detection problem on PHD filter by modelling components and parameters within it. At last we conducted the experiment on real data of forest and vegetation, and confirmed its ability and accuracy. [ABSTRACT FROM AUTHOR]

With increasing widespread use of three-dimensional data, the demand for simplified data acquisition is also increasing. The range camera, which is a simplified sensor, can acquire a dense-range image in a single shot; however, its measuring coverage is narrow and its measuring accuracy is limited. The former drawback had be overcome by registering sequential range images. This method, however, assumes that the point cloud is error-free. In this paper, we develop an integration method for sequential range images with error adjustment of the point cloud. The proposed method consists of ICP (Iterative Closest Point) algorithm and self-calibration bundle adjustment. The ICP algorithm is considered an initial specification for the bundle adjustment. By applying the bundle adjustment, coordinates of the point cloud are modified and the camera poses are updated. Through experimentation on real data, the efficiency of the proposed method has been confirmed. [ABSTRACT FROM AUTHOR]

3D models have been widely used by spread of many available free-software. On the other hand, enormous images can be easily acquired, and images are utilized for creating the 3D models recently. However, the creation of 3D models by using huge amount of images takes a lot of time and effort, and then efficiency for 3D measurement are required. In the efficiency strategy, the accuracy of the measurement is also required. This paper develops an image selection method based on network design that means surveying network construction. The proposed method uses image connectivity graph. By this, the image selection problem is regarded as combinatorial optimization problem and the graph cuts technique can be applied. Additionally, in the process of 3D reconstruction, low quality images and similarity images are extracted and removed. Through the experiments, the significance of the proposed method is confirmed. Potential to efficient and accurate 3D measurement is implied. [ABSTRACT FROM AUTHOR]

Understanding of human dynamics has drawn attention to various areas. Due to the wide spread of positioning technologies that use GPS or public Wi-Fi, location information can be obtained with high spatial-temporal resolution as well as at low cost. By collecting set of individual location information in real time, monitoring of human dynamics is recently considered possible and is expected to lead to dynamic traffic control in the future. Although this monitoring focuses on detecting anomalous states of human dynamics, anomaly detection methods are developed ad hoc and not fully systematized. This research aims to define an anomaly detection problem of the human dynamics monitoring with gridded population data and develop an anomaly detection method based on the definition. According to the result of a review we have comprehensively conducted, we discussed the characteristics of the anomaly detection of human dynamics monitoring and categorized our problem to a semi-supervised anomaly detection problem that detects contextual anomalies behind time-series data. We developed an anomaly detection method based on a sticky HDP-HMM, which is able to estimate the number of hidden states according to input data. Results of the experiment with synthetic data showed that our proposed method has good fundamental performance with respect to the detection rate. Through the experiment with real gridded population data, an anomaly was detected when and where an actual social event had occurred. [ABSTRACT FROM AUTHOR]

Recently, development of high performance CPU, cameras and other sensors on mobile devices have been used for wide variety of applications. Most of the applications require self-localization of the mobile device. Since the self-localization is based on GPS, gyro sensor, acceleration meter and magnetic field sensor (called as POS) of low accuracy, the applications are limited. On the other hand, self-localization method using images have been developed, and the accuracy of the method is increasing. This paper develops the self-localization method by integrating sensors, such as POS and cameras, on mobile devices simultaneously. The proposed method mainly consists of two parts: one is the accuracy improvement of POS data filtering, and another is development of self-localization method by integrating POS and camera. The POS data filtering combines all POS data by using Kalman filter in order to improve the accuracy of exterior orientation factors. The exterior orientation factors with POS filtering are used as initial value of ones in image-based self-localization method. The image-based self-localization method consists of feature points extraction and tracking, relative orientation, coordinates estimation of the feature points, and orientation factors updates of the mobile device. The proposed method is applied to POS data and images taken in urban area. Through experiments with real data, the accuracy improvement by POS data filtering is confirmed. The proposed self-localization method with POS and camera make the accuracy more sophisticated by comparing with only POS data filtering. [ABSTRACT FROM AUTHOR]

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions. [ABSTRACT FROM AUTHOR]

This paper aims at obtaining basic knowledge about characteristics of observation models for human tracking method as a stochastic process. As human tracking in actual cases are complicated, we cannot always use the same observation models for every situation. Thus in most cases observation models are set empirically so far. In order to achieve an efficient choice of models and parameters, understanding some advantages and disadvantages of such models regarding to observation conditions is important. In this paper we conduct a sensitive analysis on some types of observation models. In particular, we obtain both colour and range information at a railway station. We prepare six predictive distributions as well as six models and parameters for both colour and range observation models. We calculate posterior distributions of each pattern, namely 36 patterns for both colour and range models. As a sensitive analysis we compare a value of a ground truth and an expected value of posteriors. We also compare variances of predictive and posterior distributions. Through this experimental results, we confirm our analysis method is efficient to obtain information about observation models. In fact, all models analysed are good in whole. One suggestive result is that colour models can deal with a predictive error in mean values, while range models in variances. Another is that under occlusions range models show a good performance. [ABSTRACT FROM AUTHOR]

Recently, development of high performance CPU, cameras and other sensors on mobile devises have been used for wide variety of applications. Most of the applications require self-localization of the mobile device. Since the self-localization is based on GPS, gyro sensor, acceleration meter and magnetic field sensor (called as POS) of low accuracy, the applications are limited. On the other hand, self-localization method using images have been developed, and the accuracy of the method is increasing. This paper develops the self-localization method using sensors, such as POS and cameras, on mobile devices simultaneously. The proposed method mainly consists of two parts: one is the accuracy improvement of POS data in itself by POS sensor fusion based on filtering theory, and another is development of self-localization method by integrating POS and camera. The proposed method combines all POS data by using Kalman filter in order to improve the accuracy of exterior orientation factors. The exterior orientation factors based on POS sensor fusion are used as initial value of ones in image-based self-localization method. The image-based self-localization method consists of feature points extraction / tracking, coordinates estimation of the feature points, and orientation factors updates of the mobile device. The proposed method is applied to POS data and images taken in urban area. Through experiments with real data, the accuracy improvement by POS sensor fusion is confirmed. The proposed self-localization method with POS and camera make the accuracy more sophisticated by comparing with only POS sensor fusion. [ABSTRACT FROM AUTHOR]

ISPRS Technical Commission II focuses, at various scales, on geometric, radiometric and multi-temporal aspects of image- and range-based 3D surveying and modeling. Specifically, Commission II deals with image orientation, point cloud generation and processing, 3D feature extraction, dynamic and static scene analysis, sensor and data fusion, and machine learning for geospatial data analysis and big data techniques for massive data processing. Applications in the fields of mapping, infrastructure monitoring, heritage studies, space exploration, underwater photogrammetry and environmental engineering are also considered.The Volume related to Commission II contains 108 papers published in the ISPRS Archives and 20 published in the ISPRS Annals. The papers in Archives were accepted from among 111 abstract submissions and 59 full papers. Of the 108 Archives papers, 32 were full paper submissions and 76 were abstract submissions. The 20 papers in the Annals were selected from among 59 full paper submissions after going through a peer review process.In the aforementioned areas of research, the papers in this volume discuss the challenges and needs, and introduce novel photogrammetric solutions that depict the latest developments in the field.There has been a wide range of coverage of these topics and point cloud generation and processing has been the most active coverage with close to 38% of the accepted papers. This is followed by machine/deep learning methods with 31.8% that provide solutions to the semantic enrichment of images and 3D data. Research in heritage and underwater studies is represented with 9% and 6%, respectively, of the accepted papers.We believe these volumes nicely recap the state-of-the-art, current trends and possible applications of photogrammetry on a wide range of topics with a nice overview of the future research directions.On behalf of Technical Commission II, we would like to thank the local organizers of the 2021 ISPRS Congress, the members of the international program committee, all Working Group officers and all reviewers for their hard organizational work and efforts in the paper reviewing process. [ABSTRACT FROM AUTHOR]

ISPRS Technical Commission II focuses, at various scales, on geometric, radiometric and multi-temporal aspects of image- and range-based 3D surveying and modeling. Specifically, Commission II deals with image orientation, point cloud generation and processing, 3D feature extraction, scene understanding, sensor and data fusion, sensor characterization, machine learning for geospatial data analysis and big data techniques for massive data processing. Applications in the fields of mapping, industry, heritage, space, underwater and environment are considered.The Volume related to Commission II contains 182 papers published in the ISPRS Archives and 118 published in the ISPRS Annals. The Archives were accepted on the base of an abstract review from originally submitted 262 abstracts. The Annals comprise reviewed articles accepted from the originally submitted 191 full papers.Considering the aforementioned research issues, challenges and needs, the papers published depict the latest developments in methodological aspects and the most interesting applications in photogrammetry.Great interest was dedicated to machine/deep learning methods used to solve various steps of the photogrammetric pipeline and in particular for the semantic enrichment of images and 3D data. Many papers reported new models and methods to extract features, geometrical primitives and objects from data acquired by airborne and/or terrestrial sensors, including object recognition and 3D object/scene reconstruction.Various contributions referred to the utilization, integration, modeling and performance analyses of imaging and ranging sensors in the industry sector, environment and heritage fields. Cultural Heritage (including Underwater) attracted many submissions with various worldwide applications which depicted how 3D documentation is increasingly used and crucial for conservation, preservation and valorisation needs.We believe these volumes nicely recap the state of the art, current trends and possible applications in photogrammetry, with a nice overlook at the forthcoming years of investigations.On behalf of Technical Commission II, we would like to thank the local organisers of the 2020 ISPRS Congress, the members of the international program committee, all Working Group officers and all reviewers for their hard organizational work and efforts in the paper reviewing process. [ABSTRACT FROM AUTHOR]

ISPRS Technical Commission II focuses, at various scales, on geometric, radiometric and multi-temporal aspects of image- and range-based 3D surveying and modeling. Specifically, Commission II deals with image orientation, point cloud generation and processing, 3D feature extraction, scene understanding, sensor and data fusion, sensor characterization, machine learning for geospatial data analysis and big data techniques for massive data processing. Applications in the fields of mapping, industry, heritage, space, underwater and environment are considered.The Volume related to Commission II contains 182 papers published in the ISPRS Archives and 118 published in the ISPRS Annals. The Archives were accepted on the base of an abstract review from originally submitted 262 abstracts. The Annals comprise reviewed articles accepted from the originally submitted 191 full papers.Considering the aforementioned research issues, challenges and needs, the papers published depict the latest developments in methodological aspects and the most interesting applications in photogrammetry.Great interest was dedicated to machine/deep learning methods used to solve various steps of the photogrammetric pipeline and in particular for the semantic enrichment of images and 3D data. Many papers reported new models and methods to extract features, geometrical primitives and objects from data acquired by airborne and/or terrestrial sensors, including object recognition and 3D object/scene reconstruction.Various contributions referred to the utilization, integration, modeling and performance analyses of imaging and ranging sensors in the industry sector, environment and heritage fields. Cultural Heritage (including Underwater) attracted many submissions with various worldwide applications which depicted how 3D documentation is increasingly used and crucial for conservation, preservation and valorisation needs.We believe these volumes nicely recap the state of the art, current trends and possible applications in photogrammetry, with a nice overlook at the forthcoming years of investigations.On behalf of Technical Commission II, we would like to thank the local organisers of the 2020 ISPRS Congress, the members of the international program committee, all Working Group officers and all reviewers for their hard organizational work and efforts in the paper reviewing process. [ABSTRACT FROM AUTHOR]