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IEEE International Geoscience & Remote Sensing Symposium

作者： Wang, Min Huang, Jiru Cui, Qi Wang, Jie Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781509049516

We develop a new object-based image analysis (OBIA) software system, named remote-sensing knowledge finder (RSFinder), based on the region-line primitive association framework (RLPAF). In this system, straight-edge lines are promoted as line primitives for OBIA, which is fundamentally different with common OBIA systems, such as eCogntion and ENVI feature extraction module. based on region-line collaborative analyses, RsFinder can implement more powerful OBIAs than region-only-based systems.

关键词： Region-line primitive association framework object-based image analysis image segmentation high spatial resolution remote sensing

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object-based image analysis for the impact of sewage pollution in Malad Creek, Mumbai, India

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2016年第2期188卷 95页

作者： Shirke, Shivani Pinto, Shannon M. Kushwaha, Vikash K. Mardikar, Trupti Vijay, Ritesh CSIR Natl Environm Engn Res Inst Environm Syst Design & Modelling Div Nehru Marg Nagpur 440020 Maharashtra India

Today, object-based image analysis provides an option for integrating spatial information beyond conventional pixel-based classifications for high-resolution imagery. Due to its rare applicability in pollution assessment, an attempt has been made to assess the spatial extent of sewage pollution in Malad Creek, Mumbai, India. based on multiresolution segmentation of an IRS P6 (LISS IV) image and the Normalized Difference Turbidity Index (NDTI), the various water quality regions in the creek were classified. The existing literature implies that the reflectance of turbid water is similar to that of bare soil which gives positive NDTI values. In contrast to this, negative values of NDTI are observed in the present study due to the presence of organic matter which absorbs light and imparts turbidity, which is supported by the significant correlation between NDTI and turbidity. A strong relationship is observed between turbidity and water quality parameters, implying the impact of organic matter through discharges of sewage in the creek. based on the classified regions and the water quality parameters, the extent of pollution was ranked as high, moderate, low and least. The methodology developed in the present study was successfully applied on an IKONOS image for the same study area but a different time frame. The approach will help in impact assessment of sewage pollution and its spatial extent in other water bodies.

关键词： Creek object-based image analysis Segmentation Sewage pollution

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object-based image analysis for the assessment of mineral extraction in conflict regions - a case study in the Democratic Republic of the Congo 23

Object-based image analysis for the assessment of mineral ex...

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

作者： Kranz, Olaf Schoepfer, Elisabeth Sproehnle, Kristin Lang, Stefan Salzburg Univ Dept Geoinformat Z GIS Salzburg Austria Helmholtz Assoc German Res Ctr Res Sect Head Berlin Germany German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Oberpfaffenhofen Germany

In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

关键词： land cover change natural resources mining object-based image analysis remote sensing

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Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

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JOURNAL OF APPLIED REMOTE SENSING 2016年第2期10卷

作者： Hamedianfar, Alireza Shafri, Helmi Zulhaidi Mohd Univ Putra Malaysia Fac Engn Dept Civil Engn Serdang 43400 Selangor Malaysia Islamic Azad Univ Dept Surveying Engn Estahban Branch Estahban Fars Iran Islamic Azad Univ Young Researchers & Elite Club Estahban Branch Estahban Fars Iran Univ Putra Malaysia Fac Engn GISRC Serdang 43400 Selangor Malaysia

This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： data mining object-based image analysis remote sensing urban mapping WorldView-2 image

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Building Damage Detection Using object-based image analysis and ANFIS From High-Resolution image (Case Study: BAM Earthquake, Iran)

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2016年第5期9卷 1937-1945页

作者： Janalipour, Milad Mohammadzadeh, Ali Khaje Nasir Toosi Univ Technol Dept Geomat Fac Geodesy & Geomat Engn Tehran *** Iran KN Toosi Univ Technol Dept Remote Sensing Tehran *** Iran

Building damage detection after earthquake would help to rapid relief and response of disaster. In this study, an efficient-method was proposed for building damage detection in urban area after earthquake using pre-event vector map and postevent pan-sharpened high spatial resolution image. At first, preprocessing was applied on the postevent satellite image. Second, results of pixel-and object-based classifications were integrated. In the following, geometric features of buildings were extracted including area, rectangular fit (rect_fit), and convexity. A decision-making system based on these features and an adaptive network-based fuzzy inference system (ANFIS) model was designed to attain building damage degree. A comprehensive sensitivity analysis was carried out to find proper parameters of the ANFIS model leading to accurate damage results. The proposed method was tested over earthquake data set of Bam city in Iran. The results of our method indicate that an overall accuracy of 76.36% and kappa coefficient of 0.63 were achieved to identify building damage degree. The obtained results indicate that the postevent geometrical features (relative change of different damage levels with respect to each other) along with the ANFIS model can help to reach better results in building damage detection.

关键词： ANFIS model building damage map geometric features object-based image analysis remote sensing (RS) sensitivity analysis

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Mapping Robinia pseudoacacia forest health in the Yellow River delta by using high-resolution IKONOS imagery and object-based image analysis

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷

作者： Wang, Hong Lu, Kaiyu Pu, Ruiliang Hohai Univ Sch Earth Sci & Engn 8 Focheng West Rd Nanjing 211100 Jiangsu Peoples R China Univ S Florida Sch Geosci 4202 East Fowler AveNES 107 Tampa FL 33620 USA

The Robinia pseudoacacia forest in the Yellow River delta of China has been planted since the 1970s, and a large area of dieback of the forest has occurred since the 1990s. To assess the condition of the R. pseudoacacia forest in three forest areas (i.e., Gudao, Machang, and Abandoned Yellow River) in the delta, we combined an estimation of scale parameters tool and geometry/topology assessment criteria to determine the optimal scale parameters, selected optimal predictive variables determined by stepwise discriminant analysis, and compared object-based image analysis (OBIA) and pixel-based approaches using IKONOS data. The experimental results showed that the optimal segmentation scale is 5 for both the Gudao and Machang forest areas, and 12 for the Abandoned Yellow River forest area. The results produced by the OBIA method were much better than those created by the pixel-based method. The overall accuracy of the OBIA method was 93.7% (versus 85.4% by the pixel-based) for Gudao, 89.0% (versus 72.7%) for Abandoned Yellow River, and 91.7% (versus 84.4%) for Machang. Our analysis results demonstrated that the OBIA method was an effective tool for rapidly mapping and assessing the health levels of forest. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： IKONOS estimation of scale parameters geometry/topology assessment criteria object-based image analysis forest health Robinia pseudoacacia

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Extent of sewage pollution in coastal environment of Mumbai, India: an object-based image analysis

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WATER AND ENVIRONMENT JOURNAL 2015年第3期29卷 365-374页

作者： Vijay, Ritesh Kushwaha, Vikash K. Pandey, Neha Nandy, Tapas Wate, S. R. Natl Environm Engn Res Inst CSIR Environm Syst Design & Modelling Div Nagpur 440020 Maharashtra India

The coastal water quality of Mumbai is deteriorating by receiving partially treated effluent from wastewater treatment facilities, sewage discharges from ocean outfalls and discharges from point and non-point sources in the creek and coast. A novel approach of object-based image analysis has been used in this research study to assess the extent of sewage pollution in the coastal environment of Mumbai. For this, Indian Remote Sensing P6 Linear Imaging Self Scanning IV image was used for multiresolution segmentation and rule-based image classification as per normalised difference water index and normalised difference turbidity index. Water quality regions as per classification were strongly correlated with observed water quality parameters. based on classified regions and water quality parameters, extent of sewage pollution in the coast was ranked from high to least polluted. The approach developed in this methodology should be tested in similarly polluted waters to ascertain its adaptability for assessing the spatial extent of sewage pollution.

关键词： biochemical oxygen demand coastal pollution image segmentation normalised difference turbidity index object-based image analysis

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Land cover changes assessment using object-based image analysis in the Binah River watershed (Togo and Benin)

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EARTH AND SPACE SCIENCE 2015年第10期2卷 403-416页

作者： Badjana, Heou Maleki Helmschrot, Joerg Selsam, Peter Wala, Kperkouma Fluegel, Wolfgang-Albert Afouda, Abel Akpagana, Koffi Univ Abomey Calavi Grad Res Program Climate Change & Water Resources West African Sci Serv Ctr Climate Change & Adapte Cotonou Benin Univ Lome Fac Sci Lab Bot & Plant Ecol Lome Togo Univ Jena Dept Geoinformat Hydrol & Modelling Jena Germany Univ Hamburg Bioctr Klein Flottbek & Bot Garden Southern African Sci Serv Ctr Climate Change & Ad Hamburg Germany

In this study, land cover changes between 1972 and 2013 were investigated in the Binah River watershed (North of Togo and Benin) using remote sensing and geographic information system technologies. Multitemporal satellite images-Landsat MSS (1972), TM (1987), and OLI-TIRS (2013)-were processed using object-based image analysis and post-classification comparison methods including landscape metrics and changes trajectories analysis. Land cover maps referring to five main land cover classes, namely, agricultural land, forest land, savannah, settlements, and water bodies, were produced for each acquisition date. The overall accuracies were 76.64% (1972), 83.52% (1987), and 88.84% (2013) with respective Kappa statistics of 0.69, 0.78, and 0.86. The assessment of the spatiotemporal pattern of land cover changes indicates that savannah, the main vegetation type, has undergone the most dominant change, decreasing from 67% of the basin area in 1972 to 56% in 1987 and 33% in 2013. At the same time, agricultural land has significantly increased from 15% in 1972 to 24% in 1987 and 43% in 2013, while some proportions of agricultural land were converted to savannah relating to fallow agriculture. In total, more than 55% of the landscape experienced changes between 1972 and 2013. These changes are primarily due to human activities and population growth. In addition, agricultural activities significantly contributed to the increase in the number of patches, degree of division, and splitting index of forest and savannah vegetations and the decrease in their effective mesh sizes. These results indicate further fragmentation of forest and savannah vegetations between 1972 and 2013. Further research is needed to quantitatively evaluate the influences of individual factors of human activities and to separate these from the impacts of climate change-driven disturbances.

关键词： object-based image analysis land cover change land cover change trajectories landscape metrics the Binah River watershed

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Identification of Forested Landslides Using LiDar Data, object-based image analysis, and Machine Learning Algorithms

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REMOTE SENSING 2015年第8期7卷 9705-9726页

作者： Li, Xianju Cheng, Xinwen Chen, Weitao Chen, Gang Liu, Shengwei China Univ Geosci Fac Informat Engn Wuhan 430074 Peoples R China China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 Enschede Netherlands China Aero Geophys Survey & Remote Sensing Ctr La Inst Remote Sensing Method Beijing 100083 Peoples R China

For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body;(2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful;(2) FS improved classification accuracy and reduced features;(3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine;(4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments;(5) based on the classification result with an overall accuracy of 89.11% +/- 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

关键词： landslide inventory LiDar object-based image analysis machine learning the Three Gorges

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A building extraction approach for Airborne Laser Scanner data utilizing the object based image analysis paradigm

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年第0期52卷 137-148页

作者： Tomljenovic, Ivan Tiede, Dirk Blaschke, Thomas Salzburg Univ Dept Geoinformat Z GIS Schillerstr 30 A-5020 Salzburg Austria

In the past two decades object-based image analysis (OBIA) established itself as an efficient approach for the classification and extraction of information from remote sensing imagery and, increasingly, from non-image based sources such as Airborne Laser Scanner (ALS) point clouds. ALS data is represented in the form of a point cloud with recorded multiple returns and intensities. In our work, we combined OBIA with ALS point cloud data in order to identify and extract buildings as 2D polygons representing roof outlines in a top down mapping approach. We performed rasterization of the ALS data into a height raster for the purpose of the generation of a Digital Surface Model (DSM) and a derived Digital Elevation Model (DEM). Further objects were generated in conjunction with point statistics from the linked point cloud. With the use of class modelling methods, we generated the final target class of objects representing buildings. The approach was developed for a test area in Biberach an der Riss (Germany). In order to point out the possibilities of the adaptation-free transferability to another data set, the algorithm has been applied "as is" to the ISPRS Benchmarking data set of Toronto (Canada). The obtained results show high accuracies for the initial study area (thematic accuracies of around 98%, geometric accuracy of above 80%). The very high performance within the ISPRS Benchmark without any modification of the algorithm and without any adaptation of parameters is particularly noteworthy. (C) 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

关键词： object-based image analysis LiDAR Building delineation Building identification Point cloud analysis DIM generation

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