2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.     

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

Geometric-constrained multi-view image matching method based on semi-global optimization

Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models, this paper presents a geometric-constrained multi-view image matching method, based on an energy minimization framework. By employing a geometrical constraint, the cost value of the energy function was calculated from multiple images, and the cost value was aggregated in an image space using a semi-global optimization approach. A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculating cost values. It is based on the known interior orientation parameters, exterior orientation parameters, and a given elevation value. For an efficient and reliable processing of multiple remote sensing images, the proposed matching method was performed via a coarse-to-fine strategy through image pyramid. Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method. Results reveal that the multi-view image matching can improve matching reliability. Moreover, the experimental results show that the proposed method performs better than traditional methods.     

Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models

This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern.     

Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000

The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads.     

湖北宜昌重要地质遗迹资源特征及发展模式探讨

本文系统总结了湖北宜昌地区省级及省级以上地质遗迹资源,认为该地区地质遗迹分布规律主要可概括为两个方面,一是受控于地形和河流的综合作用,以各类地貌景观遗迹为特点;二是各个地质时代地层剖面齐全,古生物化石产地聚集。其中可归类为世界级地质遗迹点为5个,均以“古生物化石”为核心,分别为三峡地区埃迪卡拉生物群、长阳寒武纪清江生物群、黄花场中奥陶统大坪阶“金钉子”、王家湾上奥陶统赫南特阶“金钉子”和远安三叠系南漳—远安动物群。立足于此,文中首次详细总结了这些地质遗迹点的化石代表、科学及科普意义,并从地质遗迹自身保护和应用方式、地质遗迹载体、地学研学、地质文化的传播和文创科普产品5个方面对古生物地质遗迹资源的发展模式进行了详细探讨,从而为宜昌地区全域地质遗迹开发和旅游提供新思路。     

磁法三维建模在龙王沟磁铁矿勘探中的应用

以龙王沟磁铁矿高精度磁测数据为基础,采用小波多尺度分解方法,对化极后的磁法数据进行多尺度分解,通过功率谱法计算小波多尺度分解后的各阶细节异常的场源深度。结合地形、地质及钻孔资料,采用2.5D人机交互反演对磁测剖面上的磁铁矿体进行拟合,利用Golden Software公司开发的Voxler软件,在虚拟三维环境下实现二维小波多尺度分解数据的可视化,建立三维地质体模型,直观的预测磁铁矿体的空间范围和分布形态,实现地质、物探和地上、地下信息一体化集成展示,进行专业分析评价和辅助决策,可以有效的提高地质找矿效果。     

冻土机械切削破碎机理的研究进展

冻土开挖困难、破碎效率低是高寒地区工程建设、地基施工等面临的技术难题。冻土机械切削破碎是冻土开挖的主要方法,其机理研究是提高冻土破碎效率的前提和基础。首先总结了温度、含水率、围压等对冻土复杂力学特性的影响,进而调研分析了冻土机械切削破碎的典型切削力学模型,发现冻土切削机械破碎模式不仅与冻土力学特性密切相关,也与切削参数和刀具结构直接相关,冻土切削过程中存在着最优的切削前角（30°~60°）,且深切削和浅切削时冻土内部受力方式存在差异也会导致破坏形式的不同;温度、含水率、围压所造成的冻土力学性能变化会直接导致冻土破坏过程和切削破碎机理的改变,冻土强度随着温度降低表现出先升高然后保持稳定的特性,随着含水率升高呈现出先升高后降低的趋势,冻土破碎存在脆性、塑脆过渡及塑性等不同破坏形式。通过系统总结冻土切削破碎机理研究进展,进一步明确了冻土力学性质主要影响因素、变化特点及其切削破坏损伤特征,为冻土机械切削破碎的切削参数和切削具结构优化提供了设计依据。