Object-Based Building Boundary Extraction from Lidar Data

Lidar is a remote sensing technology that uses laser beams to generate high-accuracy, three-dimensional (3D) information of the Earth. As urban areas are developing and expanding rapidly, lidar applications such as 3D building modelling and city mapping are of increasing importance. Hence building boundary extraction is one of the main applications of lidar in civil engineering and urban planning projects. In this paper, three boundary extraction algorithms including an alpha-shape algorithm, a modified concave hull algorithm and a grid-based algorithm are tested to assess their object-by-object accuracy. The alpha-shape algorithm generates reliable boundaries for most of sample buildings, while the grid-based algorithm shows less consistency in some cases. The concave hull algorithm performs moderately with a few limitations. Advantages and disadvantages of each algorithm are identified and addressed in this paper

An On-Board Visual-Based Attitude Estimation System For Unmanned Aerial Vehicle Mapping

This paper evaluates the performances of several salient feature detectors, namely; Harris detector, Minimum Eigenvalue (MinEig), Scale Invariant Feature Transform (SIFT), Maximally Stable Extremal Region (MSER), Speeded Up Robust Feature (SURF), Features from Accelerated Segment Test (FAST), and Binary Robust Scale Invariant Keypoint (BRISK), in order to assess the suitability in the application of the proposed visual-based attitude estimation system. Throughout the experiment, three main requirements have been investigated which include Time-to-Complete (TTC), detection rate, and matching rate. It was found that SURF fulfills each of the system’s requirements. Moreover, it was also found that keypoints detection capabilities affect the processing time, and the clustering patterns in the results may assist in automated inspection of correct and false matching

GPS network-based approach to mitigate residual tropospheric delay in low latitude areas

A strong spatio-temporal variation of the wet component in the troposphere leaves us in a peculiar predicament. The residual tropospheric delay will remain in the measurements and therefore affect the estimation of related parameters. In the areas of hot and wet climate conditions, especially in the equatorial or low latitude regions, the strong tropospheric effect on GPS measurements is unquestionable. This study proposes geometric modeling through the network-based approach to mitigate the residual tropospheric delay in such regions. A part of Southeast Asia is selected as a test area for the study, which covers Malaysia and Singapore. Tests are conducted in post-processing but in the “simulating RTK� mode, and evaluated by the number of ambiguity fixes and the accuracy of the coordinate results. Network-based RTK positioning in low latitude areas has shown that the proposed technique can enhance ambiguity resolution by pivoting the ionosphere-free measurements through the mitigated residual tropospheric delay

Network-based RTK Positioning: Impact of Separating Dispersive and Non-dispersive Components on User-side Processing Strategy

The concept of network-based positioning has been extensively developed in order to better model the distance-dependent errors of GPS carrier-phase measurements. These errors can be separated into a frequency-dependent or dispersive component (e.g. the ionospheric delay) and a non-dispersive component (e.g. the tropospheric delay and orbit biases). In fact, dispersive and non-dispersive errors have different dynamic effects on the GPS network corrections. The separation of the two is useful for modelling the network corrections and can provide network users with more options for their data processing strategy. A simple running average is proposed in this paper to provide a stable network correction for the non-dispersive term. It is found that the non-dispersive correction can be used to obtain better ionosphere-free measurements, and therefore helpful in resolving the long-range integer ambiguity of the GPS carrier-phase measurements. Once the integer ambiguities have been resolved, dispersive and non-dispersive corrections can be applied to the fixed carrier-phase measurements for positioning step so as to improve the accuracy of the estimated coordinates. Instantaneous positioning, i.e. single-epoch positioning, has been tested for two regional networks: Sydney Network (SYDNET) and Singapore Integrated Multiple Reference Station (SIMRSN), Singapore. The test results have shown that the proposed strategy performs well in generating the network corrections, in fixing ambiguities and in computing a user’s position

An investigation on the best-fit models for sugarcane biomass estimation by Linear Mixed-Effect Modelling on Unmanned Aerial Vehicle-Based Multispectral Images: a case study of Australia

Due to the worldwide population growth and the increasing needs for sugar-based products, accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth. This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles (UAVs). The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments. Individual spectral bands and different combinations of the plots, growth stages, and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling. A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution. The results showed that utilizing Green, Blue, and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates. Additionally, the combination of plots and growth stages outperformed all the candidates of random effects. The proposed model outperformed the Multiple Linear Regression (MLR), Generalized Linear Model (GLM), and Generalized Additive Model (GAM) for wet and dry sugarcane biomass, with coefficients of determination (R2) of 0.93 and 0.97, and Root Mean Square Error (RMSE) of 12.78 and 2.57 t/ha, respectively. This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices (VIs) in mature growth stages

スマートフォン市場におけるロックイン戦略の検証─AppleとSamsungの戦略ポジション─

1．本研究とその背景 2．携帯端末製品市場の急変 3．AppleとSamsungの戦略ポジション（2012年） 4．Appleのシステムロックインと反作用 5．AppleとSamsungの戦略ポジション（2014年） 6．結

An Improved Parcel-Based Approach to Bruneian Geocoded Address Database

A new framework of Brunei’s national geocoded address database is proposed in this paper. The proposed framework is based on the concept of land parcel-based geocoding and deterministic record linkage, which involves three datasets: the national address database, cadastral polygons and building centroids. The technique used in the development of the framework is an improved version of land parcel-based geocoding with no matching address components since addresses are sourced from the authorised national address database. Addresses are mapped onto the centroids of building polygons resulting in formation of geocoded address points. Cadastral polygons of land parcels act as a mediator to link the address database and the building centroids using its unique key known as ‘lotnum_bc’. The proposed approach has an advantage in terms of fitting into the currently available resources. Furthermore, the proposed approach produces geocoded addresses for buildings when compared with valid addresses from the authorised address database up to the accuracy of parcel-based geocoding level. The deterministic record linkage requires validation of ‘lotnum_bc’ within the address database to ensure such an accuracy. It is expected that the proposed geocoded address database will become an integral part of the spatial data infrastructure of Brunei

Dynamic Block-Based Parameter Estimation for MRF Classification of High-Resolution Images

International audienceA Markov random field is a graphical model that is commonly used to combine spectral information and spatial context into image classification problems. The contributions of the spatial versus spectral energies are typically defined by using a smoothing parameter, which is often set empirically. We propose a new framework to estimate the smoothing parameter. For this purpose, we introduce the new concepts of dynamic blocks and class label co-occurrence matrices. The estimation is then based on the analysis of the balance of spatial and spectral energies computed using the spatial class co-occurrence distribution and dynamic blocks. Moreover, we construct a new spatially weighted parameter to preserve the edges, based on the Canny edge detector. We evaluate the performance of the proposed method on three data sets: a multispectral DigitalGlobe WorldView-2 and two hyperspectral images, recorded by the AVIRIS and the ROSIS sensors, respectively. The experimental results show that the proposed method succeeds in estimating the optimal smoothing parameter and yields higher classification accuracies when compared to the state-of-the-art methods

Smoothing parameter estimation for Markov random field classification of non-Gaussian distribution image

International audienceIn the context of remote sensing image classification, Markov random fields (MRFs) have been used to combine both spectral and contextual information. The MRFs use a smoothing parameter to balance the contribution of the spectral versus spatial energies, which is often defined empirically. This paper proposes a framework to estimate the smoothing parameter using the probability estimates from support vector machines and the spatial class co-occurrence distribution. Furthermore, we construct a spatially weighted parameter to preserve the edges by using seven different edge detectors. The performance of the proposed methods is evaluated on two hyperspectral datasets recorded by the AVIRIS and ROSIS and a simulated ALOS PALSAR image. The experimental results demonstrated that the estimated smoothing parameter is optimal and produces a classified map with high accuracy. Moreover, we found that the Canny-based edge probability map preserved the contours better than others