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object-based analysis of Airborne LiDAR Data for Building Change Detection

REMOTE SENSING 2014年第11期6卷 10733-10749页

作者： Pang, Shiyan Hu, Xiangyun Wang, Zizheng Lu, Yihui Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Guangzhou Jiantong Surveying & Mapping Technol De Guangzhou 510663 Guangdong Peoples R China

Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as "newly built", "taller", "demolished" or "lower" by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km(2) area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m(2). The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

关键词： change detection object-based analysis point cloud data building

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object-based analysis for Forest inventory

Object-based analysis for Forest inventory

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8th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

作者： Wang, Lu Chen, Erxue Li, Zenyuan Yao, Wanqiang Li, Shiming Chinese Acad Forestry Inst Forest Resources & Informat Technol Beijing Peoples R China

ISBN: (纸本)9780819498069

As widely used today, high resolution image becomes a useful data source for forest inventory because it can show detailed information of land-cover types which is so helpful in interpreting process. And in the application of high-resolution images, the toughest problem is to find the effective characteristics group to separate each class accurately. In this paper, we tried an object-based method to get the whole forest distribution of the study area. Combining segmentation and decision tree feature selection tool, we tried to find a convenient and effective way to select useful information from such many characteristics brought by. super-pixels. after segmentation. Compared with the traditional pixel-based classification method, we found that object-based method was more appropriate not only for its nearly 10% higher classification accuracy but also providing with more detailed information lying in the image data that help.

关键词： high-resolution image decision tree object-based analysis forest inventory

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object-based analysis OF LIDAR GEOMETRIC FEATURES FOR VEGETATION DETECTION IN SHADED AREAS 23

OBJECT-BASED ANALYSIS OF LIDAR GEOMETRIC FEATURES FOR VEGETA...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Lin, Yu-Ching Lin, ChinSu Tsai, Ming-Da Lin, Chun-Lin Natl Def Univ Chung Cheng Inst Technol Dept Environm Informat & Engn Taoyuan Taiwan Natl Chiayi Univ Dept Forestry & Nat Resources Chiayi 60004 Taiwan Natl Chung Shan Inst Sci & Technol Dept Informat Commun Res & Dev Taoyuan Taiwan

The extraction of land cover information from remote sensing data is a complex process. Spectral information has been widely utilized in classifying remote sensing images. However, shadows limit the use of multispectral images because they result in loss of spectral radiometric information. In addition, true reflectance may be underestimated in shaded areas. In land cover classification, shaded areas are often left unclassified or simply assigned as a shadow class. Vegetation indices from remote sensing measurement are radiation-based measurements computed through spectral combination. They indicate vegetation properties and play an important role in remote sensing of forests. Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. This study investigated three types of geometric lidar features where NDVI values fail to represent meaningful forest information. The three features include echo width, normalized eigenvalue, and standard deviation of the unit weight observation of the plane adjustment, and they can be derived from waveform data and discrete point clouds. Various feature combinations were carried out to evaluate the compensation of the three lidar features to vegetation detection in shaded areas. Echo width was found to outperform the other two features. Furthermore, surface characteristics estimated by echo width were similar to that by normalized eigenvalues. Compared to the combination of only NDVI and mean height difference, those including one of the three features had a positive effect on the detection of vegetation class.

关键词： LIDAR object-based analysis

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Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data

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Frontiers of Earth Science 2017年第1期11卷 11-19页

作者： Menglong YAN Thomas BLASCHKE Hongzhao TANG Chenchao XIAO Xian SUN Daobing ZHANG Kun FU Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Z GIS - Centre for Geoinformatics and Department for Geography and Geology University of Salzburg Salzburg A-5020 Austria Satellite Mapping Application Center State Bureau of Surveying and Mapping Beijing 100048 China China Aero Geophysical Survey and Remote Sensing Center for Land and Resources Beijing 100083 China

Airborne laser scanning （ALS） is a technique used to obtain Digital Surface Models （DSM） and Digital Terrain Models （DTM） efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of object- based analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation. We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing （ESRGG） algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.

关键词： airborne laser scanning object-based analysis surface complexity information filtering algorithm

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An Experimental Comparison for the Identification of Weeds in Sunflower Crops via Unmanned Aerial Vehicles and object-based analysis 13

An Experimental Comparison for the Identification of Weeds i...

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13th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Perez-Ortiz, Maria Antonio Gutierrez, Pedro Manuel Pena, Jose Torres-Sanchez, Jorge Hervas-Martinez, Cesar Lopez-Granados, Francisca CSIC Inst Sustainable Agr Cordoba 14080 Spain Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9783319192581;9783319192574

Weed control in precision agriculture refers to the design of site-specific control treatments according to weed coverage and it is very useful to minimise costs and environmental risks. The crucial component is to provide precise and timely weed maps via weed monitoring. This paper compares different approaches for weed mapping using imagery from Unmanned Aerial Vehicles in sunflower crops. We explore different alternatives, such as object-based analysis, which is a strategy that is spreading rapidly in the field of remote sensing. The usefulness of these approaches is tested by considering support vector machines, one of the most popular machine learning classifiers. The results show that the object-based analysis is more promising than the pixel-based one and demonstrate that both the features related to vegetation indexes and those related to the shape of the objects are meaningful for the problem.

关键词： Unmanned Aerial Vehicles object-based analysis Weed mapping Image segmentation Support vector machines

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A classification framework with multi-level fusion of object-based analysis and convolutional neural network: a case study for land use classification in mining areas

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GEO-SPATIAL INFORMATION SCIENCE 2024年

作者： Xing, Jianghe Li, Zhenwei Li, Jun Du, Shouhang Li, Wei Zhang, Chengye China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing Peoples R China Minist Nat Resources Key Lab Coupling Proc & Effect Nat Resources Eleme Beijing Peoples R China

To enhance the accuracy of land use classification in mining areas, the object-based Convolutional Neural Network (OCNN) method has been widely used. However, existing researches tend to neglect the importance of decision-level fusion, focusing only on feature-level fusion. This study proposes a new classification framework with Multi-level Fusion of object-based analysis and CNN (MFOCNN) to achieves high-accuracy land use classification in mining areas. First, Simple Linear Iterative Cluster (SLIC) is employed to generate image objects, which serve as the basic unit for classification. Second, an improved DenseNet is proposed to extract deep features from image patches, which represent image objects, and provide the classification result. Third, handcrafted features including spectral, textural and geometric of the image objects are extracted and fused with the deep features to obtain the classification result with random forest classifier. Finally, the Dempster-Shafer (DS) evidence theory is applied to fuse the two previously described classification results at the decision-level to obtain the final result. Experiments conducted in the mining areas of Erdos using Gaofen-6 images demonstrate that the proposed MFOCNN achieves the best visual performance and accuracy among all tested methods. The MFOCNN, with its feature-level fusion and decision-level fusion, significantly improves the accuracy of land use classification in mining areas. The results suggest that the proposed MFOCNN is a promising method for achieving high-accuracy land use classification in mining areas.

关键词： Mining areas land use classification object-based analysis DenseNet

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Assessing environmental conditions associated with spatially varying rainfall structure of North Atlantic tropical cyclones: An object-based climatological analysis

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INTERNATIONAL JOURNAL OF CLIMATOLOGY 2023年第12期43卷 5464-5484页

作者： Zhou, Yao Zhu, Laiyin Matyas, Corene J. Luan, Hui Tang, Jingyin Embry Riddle Aeronaut Univ Dept Appl Aviat Sci Daytona Beach FL USA Western Michigan Univ Dept Geog Environm & Tourism Kalamazoo MI USA Univ Florida Dept Geog Gainesville FL USA Univ Oregon Dept Geog Eugene OR USA Amazon Web Serv Inc Seattle WA USA Embry Riddle Aeronaut Univ Dept Appl Aviat Sci 1 Aerosp Blvd Daytona Beach FL 32114 USA

This study utilizes geographic information system-based shape analyses with spatial regressions to examine the spatial variations in the relationships between North Atlantic TC rain field patterns and the environmental conditions. We measure the area, solidity, dispersion and closure of rain fields associated with North Atlantic tropical cyclones (TCs) during 1998-2014 from satellite-based rain rate estimates. The potential contributing factors examined include storm intensity, translational speed, sea surface temperature, total precipitable water, upper-tropospheric divergence, deep-layer vertical wind shear and distance to land. Spatial metrics of rainfall, TC attributes and environmental conditions are aggregated onto a hexagon grid, and the average values are used in regression models. Spatial autoregressive regression and geographically weighted regression are applied to investigate the relationships between contributing factors and rainfall patterns. Storm intensity is positively correlated with area, solidity and closure and negatively correlated with dispersion. Higher moisture and larger upper-level divergence contribute to a larger rainfall field with a more dispersed and less solid pattern, especially over the western Gulf and the western Caribbean. Wind shear and storm motion affect rain field patterns differently depending on the juxtaposition and relative magnitude of storm motion and wind shear vectors and thus exhibit more regional variations. This research further highlights the importance of incorporating a spatial component into the research on TC rainfall climatology. An improved understanding of basin-wide and sub-basin-wide relationships between TC rain fields and contributing factors would benefit rainfall forecasting and hazard mitigation.

关键词： geographic information system North Atlantic object-based analysis rain fields spatial regression tropical cyclones

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object-based SYSTEM-analysis OF REAL-TIME SYSTEMS THROUGH APPLICATION OF UNIVERSE-OF-DISCOURSE GRAPHS WITH TOP-LEVEL object IDENTIFICATION

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INFORMATION AND SOFTWARE TECHNOLOGY 1992年第6期34卷 355-362页

作者： OGREN, I Romet AB Fridhem 2362 S-76040 Väddö Sweden

Today's methods for system development have problems mainly in the area of user understanding and in education costs. A simple method for object-based analysis is shown using entity-relationship graphs to model the universe of discourse. The graphs include two couplings between objects in the real world and objects in the information system: the representational coupling and the influential coupling. Through this 'double coupling', a user can easily understand how a system is intended to work in its environment. It is further shown how the work is continued from analysis into identification of top-level objects in a design solution. As an example to illustrate the principles of object-based system analysis, a problem about flight-plan handling within an air traffic control system is described.

关键词： SYSTEM DEVELOPMENT SYSTEM analysis METHODS object-based analysis GRAPHS

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Semantic object-based urban scene analysis for feature fusion of VHR imagery and Lidar DSM

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第4期17卷 1723-1731页

作者： Mahmoudi, Fatemeh Tabib Shahid Rajaee Teacher Training Univ Fac Civil Engn Dept Geomat POB 16785-163 Tehran Iran

More complexities in urban areas due to the existence of buildings with roofs made of various materials, roads and parking lots, diverse plant species and vehicles along with developed very high spatial resolution (VHR) remote sensing imageries lead to increase the difficulties of urban objects recognition. Defining human cognition semantic categories of land cover objects is proposed in this paper to improve the object-based scene analysis. After performing the segmentation algorithm, semantically feature fusion of the VHR imagery and Lidar DSM is performed in the context of object-based procedures. For this reason, high-level semantics composed of human cognition object categorization and visual-conceptual descriptions of land cover objects in the complex urban scene is used for low-level features measurement followed by classification rules generation. The pan-sharpened WorldView-2 satellite image and Lidar DSM over a complex urban area in Rio de Janeiro (Brazil) are used to evaluate the capabilities of the proposed semantic object-based scene analysis. The evaluation of the obtained object recognition results shows the 93.98% overall accuracy and 0.937 Kappa coefficient. Comparing the obtained semantic object recognition results with non-semantic object-based procedures indicates the improvements in overall accuracy for 20.68 and 2.18% in object-based spectral and object-based spectral-structural classifications, respectively. Increasing the true positive pixels of the spectrally and height similar object categories (such as buildings and road), and using threshold training without requiring high-dimensional training samples are some of the advantages of the proposed semantic object-based scene analysis method.

关键词： High-level semantic object-based analysis Low-level features Urban scenes

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Forest Structure and Stand Characteristics Influence the Space Use and Fine-Scale Movements of Fishers (Pekania pennanti)

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ANIMAL CONSERVATION 2025年第0期

作者： Martin, Marie E. Moriarty, Katie M. Hayner, Stephen Fiorella, Maria Ducey, Craig D. Hollen, Bruce Matthews, Sean M. Oregon State Univ Inst Nat Resources Corvallis OR 97331 USA Natl Council Air & Stream Improvement Inc Corvallis OR USA USDI Bur Land Management State Off Portland OR USA

Understanding animal space use can inform management and conservation, particularly in landscapes where conservation of imperiled species must be balanced with resource use by humans. We used GPS collar data to evaluate fisher (Pekania pennanti) space use and behavior at multiple spatial scales in relation to a multi-scale vegetation structure in the southern Cascade mountains of Oregon, USA. From 2015 to 2018, we estimated space use of 9 individual fishers collared during 25 deployments. To evaluate whether the vegetation structure affected fisher behavior within use areas, we modeled variation in motion variance and estimated fine-scale space use using step selection functions, including evaluating where fishers exhibit selection for corridor-type movements. Fisher use areas exhibited greater canopy cover, heterogeneity, and canopy height than stands available across the study landscape. Fishers exhibited higher motion variance as canopy cover and canopy height decreased, with the strongest effects in areas with < 26% canopy cover or < 23 m canopy height. Fine-scale habitat selection by fishers was positively associated with higher canopy cover and height, and fisher corridor-type movements were more likely to occur in symmetrically shaped, larger forest stands. Importantly, we found that fishers used greater canopy cover and height across spatial scales but exhibited asynchronous responses to structural heterogeneity at different spatial scales. At fine scales, fishers selected against heterogeneity in canopy cover and height and areas closer to stand edges and exhibited more variable movement in open or early-seral areas. Conversely, fisher space use at coarser scales was positively related to heterogeneity in forest cover and the shape and size of forest stands. Through this work, we highlight the importance of scale-dependent context when considering the effects of forest structure on species' space use and illustrate vegetation and patch characteristics that

关键词： connectivity fine-scale space use forest structure habitat selection light detection and ranging object-based analysis Pekania pennanti step selection

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