- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Spatio-statistical optimization of image segmentation process for building footprint extraction using very high-resolution WorldView 3 satellite data
摘要： Segmentation process in building footprint extraction using object-based image analysis (OBIA) is crucial due to several factors, such as the spatial and spectral resolution of remote sensing images and the complexity of geo-objects. Consequently, the selection of suitable parameters to ensure the best segmentation quality remains a challenge. To overcome this issue, a spatio-statistical optimization technique that combines the Taguchi statistical method and a spatial plateau objective function (POF) was developed to extract building footprint from high-resolution Worldview 3 (WV3) satellite data. Initially, the Taguchi statistical method was used to design the orthogonal array of 25 experiments with three segmentation parameters, namely, scale, shape, and compactness, each having five varying factor level in the orthogonal array and the calculated POF was merged to produce main effects and interaction plots for signal-to-noise ratios (SNR), whereby the smaller-is-better and larger-is-better options of the Taguchi’s SNR were tested on each parameter to maximize their effects. After that, the segmentation quality obtained from the proposed method was assessed by comparing with the benchmark method introduced by Dragut and result indicates that the proposed method was better than the benchmark method. Subsequently, the final optimal parameters were used for segmentation process in eCognition and the image object was classified into five land cover classes (building, road, water, trees, and grass) by using a supervised non-parametric statistical learning technique, Support Vector Machine (SVM) classifier. Finally, the building features was extracted, and the detection accuracy was evaluated based on receiver operating characteristics (ROC). Result shows the area under ROC curve (AUC) of 0.804 with p < 0.0001 at 95% confidence level. This verifies that the proposed method is effective for building detection with high accuracy and the integration of Taguchi and objective function managed to determine the optimal segmentation parameters. Optimization segmentation parameters can later be applied to distinguish roof materials and conditions.
关键词： building footprint,image segmentation,spatio-statistical optimization,OBIA,Taguchi method
更新于2025-09-19 17:15:36
-
[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Experimental Multiphase Estimation in an Integrated Reconfigurable Multi-Arm Interferometer
摘要： The main objective of this research was to establish a semiautomated object-based image analysis (OBIA) methodology for locating landslides. We have detected and delineated landslides within a study area in north-western Iran using normalized difference vegetation index (NDVI), brightness, and textural features derived from satellite imagery (IRS-ID and SPOT-5) in combination with slope and ?ow direction derivatives from a digital elevation model (DEM) and topographically oriented gray-level cooccurrence matrices (GLCMs). We utilized particular combinations of these information layers to generate objects by applying multiresolution segmentation in a sequence of feature selection and object classi?cation steps. The results were validated by using a landslide inventory database including 109 landslide events. In this study, a combination of these parameters led to a high accuracy of landslide delineation yielding an overall accuracy of 93.07%. Our results con?rm the potential of OBIA for accurate delineation of landslides from satellite imagery and, in particular, the ability of OBIA to incorporate heterogeneous parameters such as DEM derivatives and surface texture measures directly in a classi?cation process. The study contributes to the establishment of geographic object-based image analysis (GEOBIA) as a paradigm in remote sensing and geographic information science.
关键词： object-based image analysis (OBIA),textural rule-based classi?cation,gray-level cooccurrence matrix (GLCM),landslide mapping,remote sensing,GIScience
更新于2025-09-16 10:30:52
-
Object-Based Crack Detection and Attribute Extraction From Laser-Scanning 3D Profile Data
摘要： Cracks in 3D pavement data often show poor continuity, low contrast and different depths, which bring great challenges to related application. Recently, crack attributes, e.g. depth and width have attracted attention of highway agencies for maintenance decision-makings, but few studies have been conducted on crack attributes. This paper presents object-based image analysis (OBIA) method for crack detection and attribute extraction from laser-scanning 3D pro?le data with elevation accuracy about 0.25 mm. Firstly, a high-pass ?lter designed for pavement components in our previous research was applied to remove the ?uctuation posture in 3D data, and then the smallest of-constant false-alarm rate algorithm was used to acquire lower point sets, including crack seeds and lower textures. Secondly, the objects were represented by above obtained 3D point sets and OBIA, especially, the depth statistics, shape and topological features of objects were described. Moreover, to enhance crack objects and remove texture objects gradually, multi-scale object selections and merges were conducted according to the local statistical characteristics differences of objects. Thirdly, the objects’ orientation attributes were combined with tensor voting to connect and infer ?nal crack objects, and then the object-level crack depth attributes could be extracted. The experimental results demonstrated that proposed method achieved average buffered Hausdorff scores of 94.39, Recall of 0.92 and F-value of 0.91 for crack detection on 30 real measured 3D asphalt pavement data. Furthermore, crack depth attributes can be extracted at different scales according requirements, the obtained location and depth attributes provide more comprehensive information for pavement maintenances.
关键词： Laser-scanning 3D,crack detection,crack attribute,tensor voting,OBIA
更新于2025-09-12 10:27:22
-
Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery
摘要： This study aims to investigate the combined use of two types of remote sensing data — ALS derived and digital aerial photogrammetry data (based on imagery collected by airborne UAV sensors) — along with intensive field measurements for extracting and predicting tree and stand parameters in even-aged mixed forests. The study is located in South West Romania and analyzes data collected from mixed-species plots. The main tree species within each plot are Norway spruce (Picea abies L. Karst.) and Beech (Fagus sylvatica L.). The ALS data were used to extract the digital terrain model (DTM), digital surface model (DSM) and normalized canopy height model (CHM). Object-Based Image Analysis (OBIA) classification was performed to automatically detect and separate the main tree species. A local filtering algorithm with a canopy-height based variable window size was applied to identify the position, height and crown diameter of the main tree species within each plot. The filter was separately applied for each of the plots and for the areas covered with Norway spruce and beech trees, respectively (i.e. as resulted from OBIA classification). The dbh was predicted based on ALS data by statistical Monte Carlo simulations and a linear regression model that relates field dbh for each tree species with their corresponding ALS-derived tree height and crown diameter. The overall RMSE for each of the tree species within all the plots was 5.8 cm for the Norway spruce trees, respectively 5.9 cm for the beech trees. The results indicate a higher individual tree detection rate and subsequently a more precise estimation of dendrometric parameters for Norway spruce compared to beech trees located in spruce-beech even-aged mixed stands. Further investigations are required, particularly in the case of choosing the best method for individual tree detection of beech trees located in temperate even-aged mixed stands.
关键词： Monte Carlo simulation,ALS,Forest inventory,UAV,OBIA
更新于2025-09-11 14:15:04
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Spatially Precise Contextual Features Based on Superpixel Neighborhoods for Land Cover Mapping with High Resolution Satellite Image Time Series
摘要： High resolution image time series as those provided by Sentinel-2 allow to target semantically rich nomenclatures for land cover mapping. However, at 10m resolution, pixel based classification fails to correctly identify some classes for which pixel context is discriminative. Recent advances in deep convolutional neural networks show promising results to tackle this problem, but the lack of complete annotation over large areas, the computational cost and the dimensionality of the feature space (much larger than those used in computer vision) does not allow to use these approaches in operational mapping applications yet. Contextual information can be calculated by applying a fixed-size neighborhood filter, but this can cause the loss of linear objects and the rounding of sharp corners. In Object Based Image Analysis, segmentation is used to extract objects for calculating contextual features while maintaining the high-frequency elements in the image. However, these do not necessarily include spectrally diverse pixels in a neighborhood, which can be relevant for characterizing the context. Superpixels place themselves in between the fixed-neighborhood and the object-based methods, in that they include spectrally diverse pixels in the same segment by imposing size and compacity constraints, while remaining adaptive to the natural boundaries in the image. This study assesses and compares the ability of these three types of neighborhood to improve classification performance on context-dependent classes, in a high-resolution Sentinel-2 time series land cover mapping problem.
关键词： OBIA,Land Cover Mapping,Contextual features,Superpixel,Time-series
更新于2025-09-09 09:28:46
-
LiDAR and Orthophoto Synergy to optimize Object-Based Landscape Change: Analysis of an Active Landslide
摘要： Active landslides have three major effects on landscapes: (1) land cover change, (2) topographical change, and (3) above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) are used in combination with multi-temporal orthophotos to quantify these changes between 2006 and 2012, caused by an active deep-seated landslide near the village of Doren in Austria. Land-cover is classified by applying membership-based classification and contextual improvements based on the synergy of orthophotos and LiDAR-based elevation data. Topographical change is calculated by differencing of LiDAR derived digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover classification accuracies were improved from 65% (using only LiDAR) and 76% (using only orthophotos) to 90% (using data synergy) for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The increased accuracies demonstrate the effectiveness of using data synergy of LiDAR and orthophotos using object-based image analysis to quantify landscape changes, caused by an active landslide. The method has great potential to be transferred to larger areas for use in landscape change analyses.
关键词： data synergy,above ground biomass,LiDAR,Vorarlberg,Landslide,orthophotos,OBIA,land cover change
更新于2025-09-09 09:28:46
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Comparison of Land Cover Maps Using High Resolution Multispectral and Hyperspectral Imagery
摘要： Land cover information is a fundamental parameter in a wide range of applications like urban growth, land degradation, climate change, food security, environmental sustainability, etc. In this context, remote sensing satellites can provide valuable data to allow the generation of thematic maps. On the other hand, the recent availability of hyperspectral sensors on board aircrafts and drones offers an opportunity to improve the resolution and accuracy of land cover maps. In island territories, where land is usually a scarce resource, the need of very high spatial resolution (VHR) is essential. In this context, we have generated VHR land cover maps using multispectral Worldview data and hyperspectral airborne CASI information. In particular, after corrections and pansharpening enhancements, we have analyzed pixel-based and object-based classification approaches using different input band combinations. We have compared the performance when using multispectral or hyperspectral imagery and its robustness depending on the quality of the training samples considered.
关键词： hyperspectral,OBIA classification,CASI,support vector machines,Land use land cover
更新于2025-09-09 09:28:46