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We propose an approach for estimating non-rigid correspondences between two shapes that can handle articulation and deformation of the surfaces to be matched. It operates on open or closed surfaces represented by point clouds and, therefore, it can be applied on other representations that can be converted into point clouds. Our method is capable of automatically discovering the articulated parts of the surface without requiring knowledge of the topology or the number of rigid parts. Processing begins by estimating potential sparse correspondences between the source and the target surface. These are used to align the largest corresponding parts of the two surfaces. Fragments of the surface that are not consistent with this alignment generate part hypotheses on which the algorithm is applied recursively. We present qualitative and quantitative results on four datasets comprising open and closed surfaces. [ABSTRACT FROM AUTHOR]

Recent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection and ranging (lidar) points in areas covered by densely packed buildings or dense vegetation is difficult. In this paper, we introduce a new hierarchical moving curve-fitting filter algorithm that is designed to automatically and rapidly filter lidar data to permit automatic DTM generation. This algorithm is based on fitting a second-degree polynomial surface using flexible tiles of moving blocks and an adaptive threshold. The initial tile size is determined by the size of the largest building in the study area. Based on an adaptive threshold, non-ground points and ground points are classified and labelled step by step. In addition, we used a multi-scale weighted interpolation method to estimate the bare-earth elevation for non-ground points and obtain a recovered terrain model. Our experiments in four study areas showed that the new filtering method can separate ground and non-ground points in both urban areas and those covered by dense vegetation. The filter error ranged from 4.08% to 9.40% for Type I errors, from 2.48% to 7.63% for Type II errors, and from 5.01% to 7.40% for total errors. These errors are lower than those of triangulated irregular network filter algorithms. [ABSTRACT FROM PUBLISHER]

Highlights • We proposed an up-scaling method of assimilating reflectance into model. • Constraint of key input parameters for PROSAIL through WOFOST output. • A faster data-assimilation strategy for 30-m resolution was developed. • Assimilating up-scaling reflectance achieved the highest yield estimation. Abstract To estimate regional-scale winter wheat (Triticum aestivum) yield, we developed a data-assimilation scheme that assimilates remotely sensed reflectance into a coupled crop growth–radiative transfer model. We generated a time series of 8-day, 30-m-resolution synthetic Kalman Smoothed reflectance by combining MODIS surface reflectance products with Landsat surface reflectance using a KS algorithm. We evaluated the assimilation performance using datasets with different spatial and temporal scales (e.g., three dates for the 30-m Landsat reflectance, 8-day and 1-km MODIS surface reflectance, and 8-day and 30-m synthetic KS reflectance) into the coupled WOFOST–PROSAIL model. Then we constructed a four-dimensional variational data assimilation (4DVar) cost function to account for differences between the observed and simulated reflectance. We used the shuffled complex evolution–University of Arizona (SCE-UA) algorithm to minimize the 4DVar cost function and optimize important input parameters of the coupled model. The optimized parameters were used to drive WOFOST and estimate county-level winter wheat yield in a region of China. By assimilating the synthetic KS reflectance data, we achieved the most accurate yield estimates (R 2 = 0.44, 0.39, and 0.30; RMSE = 598, 1288, and 595 kg/ha for 2009, 2013, and 2014, respectively), followed by Landsat reflectance (R 2 = 0.21, 0.22, and 0.33; RMSE = 915, 1422, and 637 kg/ha for 2009, 2013, and 2014, respectively) and MODIS reflectance (R 2 = 0.49, 0.05, and 0.22; RMSE = 1136, 1468, and 700 kg/ha for 2009, 2013, and 2014, respectively) at the county level. Thus, our method improves the reliability of regional-scale crop yield estimates. [ABSTRACT FROM AUTHOR]

As increasing number of studies for shape measurement purposes in livestock farming by using consumer depth cameras, many software have been developed in order to measure livestock conformation. However, many of these softwares were designed only for specific livestock or body part of specific livestock with very limited body measurements. To be more flexible and general compared to the current software provided in the literature, an interactive software LSSA_CAU is developed to estimate body measurements of livestock based on 3d point clouds data. Livestock with similar forms of cows or pigs and standing with her head forward is assumed for designing algorithm used in LSSA_CAU. This software provides a set of tools for loading, rendering, segmenting, pose normalizing, measuring point clouds data of whole body surface of livestock in a semiautomatic manner. In order to validate the software, both synthetic and real world point clouds data of livestock were processed by using the LSSA_CAU. Our experiments show that the proposed software generalizes well across livestock species and supports customized body measurements. An updated LSSA_CAU version can be downloaded freely from https://github.com/LiveStockShapeAnalysis to livestock industry and research. [ABSTRACT FROM AUTHOR]

Abstract: In object-oriented classification approaches, small sample size and high dimensionality of features are the two main characteristics. In order to effectively use the rich features of the image objects, in this study, the Relief algorithms were improved in terms of aspects of randomly drawing samples, the influence of sample quantity variance, and iteration times to evaluate the features. Through experiments in WorldView-2 images, the two classification results were contrasted between 36 dimension feature sets selected from Initials and initial 111 dimension feature sets. The results showed that the dimension disaster was avoided efficiently, while the precision and speed of classification were improved as well. Respectively, the overall accuracy and kappa coefficient using the optimized feature sets with the improved Relief algorithm were increased by 6% and 11% compared with using all feature sets. Therefore, the feature space of object-oriented classification is effectively optimized with the proposed methods. [Copyright &y& Elsevier]