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Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-based Classification of Polarimetric SAR and object-based image analysis

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REMOTE SENSING 2014年第12期6卷 11977-12004页

作者： Plank, Simon Mager, Alexander Schoepfer, Elisabeth German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD D-82234 Oberpfaffenhofen Germany

In developing countries, there is a high correlation between the dependence of oil exports and violent conflicts. Furthermore, even in countries which experienced a peaceful development of their oil industry, land use and environmental issues occur. Therefore, independent monitoring of oil field infrastructure may support problem solving. Earth observation data enables fast monitoring of large areas which allows comparing the real amount of land used by the oil exploitation and the companies' contractual obligations. The target feature of this monitoring is the infrastructure of the oil exploitation, oil well pads-rectangular features of bare land covering an area of approximately 50-60 m x 100 m. This article presents an automated feature extraction procedure based on the combination of a pixel-based unsupervised classification of polarimetric synthetic aperture radar data (PolSAR) and an object-based post-classification. The method is developed and tested using dual-polarimetric TerraSAR-X imagery acquired over the Doba basin in south Chad. The advantages of PolSAR are independence of the cloud coverage (vs. optical imagery) and the possibility of detailed land use classification (vs. single-pol SAR). The PolSAR classification uses the polarimetric Wishart probability density function based on the anisotropy/entropy/alpha decomposition. The object-based post-classification refinement, based on properties of the feature targets such as shape and area, increases the user's accuracy of the methodology by an order of a magnitude. The final achieved user's and producer's accuracy is 59%-71% in each case (area based accuracy assessment). Considering only the numbers of correctly/falsely detected oil well pads, the user's and producer's accuracies increase to even 74%-89%. In an iterative training procedure the best suited polarimetric speckle filter and processing parameters of the developed feature extraction procedure are determined. The high transferability of the me

关键词： SAR polarimetry object-based image analysis feature extraction natural resources monitoring oil exploitation

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Detailed Lacustrine Calving Iceberg Inventory from Very High Resolution Optical imagery and object-based image analysis

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REMOTE SENSING 2020年第11期12卷 1807-1807页

作者： Podgorski, Julian Petlicki, Michal Polish Acad Sci Inst Geophys Ul Ksiecia Janusza 64 PL-01452 Warsaw Poland Ctr Estudios Cient Av Prat 514 Valdivia 5110466 Chile

In the field of iceberg and glacier calving studies, it is important to collect comprehensive datasets of populations of icebergs. Particularly, calving of lake-terminating glaciers has been understudied. The aim of this work is to present an object-based method of iceberg detection and to create an inventory of icebergs located in a proglacial lagoon of San Quintin glacier, Northern Patagonia Icefield, Chile. This dataset is created using high-resolution WorldView-2 imagery and a derived DEM. We use it to briefly discuss the iceberg size distribution and area-volume scaling. Segmentation of the multispectral imagery produced a map of objects, which were classified with use of Random Forest supervised classification algorithm. An intermediate classification product was corrected with a ruleset exploiting contextual information and a watershed algorithm that was used to divide multiple touching icebergs into separate objects. Common theoretical heavy-tail statistical distributions were tested as descriptors of iceberg area and volume distributions. Power law models were proposed for the area-volume relationship. The proposed method performed well over the open lake detecting correctly icebergs in all size bands except 5-15 m(2) where many icebergs were missed. A section of the lagoon with ice melange was not reliably mapped due to uniformity of the area in the imagery and DEM. The precision of the DEM limited the scaling effort to icebergs taller than 1.7 m and larger than 99 m(2), despite the inventory containing icebergs as small as 4 m(2). The work demonstrates viability of object-based analysis for lacustrine iceberg detection and shows that the statistical properties of iceberg population at San Quintin glacier match those of populations produced by tidewater glaciers.

关键词： object-based image analysis WorldView-2 glacier calving lacustrine calving Northern Patagonia Icefield iceberg power law random forest

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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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Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey)

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COMPUTERS & GEOSCIENCES 2012年第1期38卷 87-98页

作者： Aksoy, Beliz Ercanoglu, Murat Hacettepe Univ Geol Engn Dept TR-06800 Ankara Turkey

This study presents a data-driven and semiautomatic classification system carried out by object-based image analysis and fuzzy logic in a selected landslide-prone area in the Western Black Sea region of Turkey. In the first stage, a multiresolution segmentation process was performed using Landsat ETM+ satellite images of the study area. The model was established on 5235 image objects obtained by the segmentation process. A total of 70 landslide locations and 10 input parameters including normalized difference vegetation index, slope angle, curvature, brightness, mean band blue, asymmetry, shape index, length/width ratio, gray level co-occurrence matrix, and mean difference to infrared band were considered in the analyses. Membership functions were used to classify the study area by five fuzzy operators such as "and", "or", "mean arithmetic", "mean geometric", and "algebraic product". In order to assess the performances of the so-produced maps, 700 image objects, which were not used in the model, were taken into consideration. based on the results, the map produced by "fuzzy and" operator performed better than those classified by the other fuzzy operators. The proposed methodology applied in this study may be useful for decision makers, local administrations, and scientists interested in landslides. It may also be useful in landslide-prone areas for planning, management, and regional development purposes. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fuzzy logic Landslide object-based image analysis Segmentation

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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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Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年 169卷 320-336页

作者： Tang, Yunwei Qiu, Fang Jing, Linhai Shi, Fan Li, Xiao Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 South Rd Beijing 100094 Peoples R China Univ Texas Dallas Geospatial Informat Sci 800 West Campbell Rd Richardson TX 75080 USA

object-based image analysis (OBIA) has been widely used to classify high spatial resolution (HSR) imagery. In a traditional OBIA, object-level statistical summaries such as mean values are usually used for classification. This implies that the spectral values within objects follow a Gaussian distribution. However, the pixel values in an object do not necessarily conform to a Gaussian distribution because of within object spectral heterogeneity. Consequently, these statistical summaries may misrepresent the features of the object. This shortcoming is addressed in this paper by integrating both the spectral variability and the spatial distribution of the pixels within objects to improve the traditional object-based image classification. The spectral variability is represented by histograms of the pixel values in the object, and the spatial distribution is characterized by the binary spatial covariogram of these pixels. To construct a binary spatial covariogram, a principal component analysis (PCA) is first applied to compress multiple bands into one, and the Otsu thresholding is then performed to generate a binary map reflecting the spatial configuration of the pixels. Spatial covariance is then computed for this binary map and plotted with different lag distances to derive the binary spatial covariogram. Our proposed model utilizing curves composed of the spectral histograms and binary spatial covariogram (referred to as the His-Cov model) are then used for classification based on curve matching approaches. The integration of spectral variability and spatial distribution of the pixels in the object produced superior results to curve matching approaches based on spectral variability alone and to traditional OBIA based on spectral and spatial features of the objects when classifying complex land use types in urban environments.

关键词： object-based image analysis Curve matching His-Coy Spatial covariogram Land cover/land use classification High resolution

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Quantifying and characterizing the dynamics of urban greenspace at the patch level: A new approach using object-based image analysis

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REMOTE SENSING OF ENVIRONMENT 2018年 204卷 94-108页

作者： Wang, Jing Zhou, Weiqi Qian, Yuguo Li, Weifeng Han, Lijian Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Accurate and quantitative description of spatial pattern of urban greenspace and its change over time is crucial for understanding ecosystem service provision and urban sustainability. Patches are the basic management and planning units in urban landscapes, and urban designers and planners and natural resource managers frequently seek tools to understand changes at the patch level. High spatial resolution imagery has been increasingly used to measure fine-scale changes of percent cover of urban greenspace. However, few studies have investigated the dynamics of urban greenspace at the patch level, to quantify the process of growth, shrinking, or disappearance of patches. Here, we developed a new approach to quantify and characterize urban greenspace change at the patch level, by implementing object-based image analysis. We used2.5 m resolution ALOS and SPOT image data captured in 2005 and 2009, and tested the new approach in Beijing, China. With this approach, we identify different types of change in patches of urban greenspace, such as expansion and shrinking, newly created, and totally lost, in addition to gain and loss information. The results show: 1) 9.25% of the total area (6179 ha) was generated from expansion of extant patches, while 5.36% (3582 ha) was lost due to shrinking of patches;2) 0.91% (607 ha) of urban greenspace was totally removed, and 1.23% (821 ha) was newly created;and 3) Urban greenspace with different ranges of patch size, from 0.01 to 1000 ha, all experienced large changes, with a tendency for small-sized patches (< 1 ha) to be totally removed. This new approach provides an effective tool for better understanding changes of urban greenspace, and results from this study provide important insights for planners and managers to devise target-specific measures and plans to more effectively protect urban green space.

关键词： Urban greenspace Patch dynamic object-based image analysis Ecosystem services Beijing

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Utilization of Improved Annotations From object-based image analysis as Training Data for DeepLab V3+Model: A Focus on Road Extraction in Very High-Resolution Orthophotos

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IEEE ACCESS 2024年 12卷 67910-67923页

作者： Sussi Husni, Emir Yusuf, Rahadian Budi Harto, Agung Suwardhi, Deni Siburian, Arthur Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia Telkom Univ Fac Elect Engn Dept Telecommun Bandung 40257 Indonesia Inst Teknol Bandung Fac Earth Sci & Technol Bandung 40132 Indonesia

Road extraction plays a crucial role in various sectors, including transportation systems, disaster relief distribution, and urban planning, necessitating a more efficient method than the current labor-intensive approach. The conventional process, reliant on operator efforts, proves costly, time-consuming, and energy intensive. To address this, the employment of deep learning models such as DeepLab V3+ are done, leveraging encoders like ResNet 50, ResNet 101, and MobileNet V2, known for their effectiveness in deep learning applications. Although Deep Learning has demonstrated faster and more automated road extraction, the bottleneck persists in the manual creation of training data through road annotations by operators. Our research focuses on accelerating road extraction by incorporating enhanced annotations from object-based image analysis (OBIA) along with organic annotations into the training data to expedite dataset creation while ensuring extraction model accuracy. Specifically, we investigate the optimal ratio of synthetic to organic annotations that yields the highest road extraction accuracy. Moreover, we enhance OBIA-derived road annotations and regulate their integration into the training data. Our findings reveal an optimal composition of 25% for OBIA annotations and 50% for improved OBIA annotations, as exceeding these numbers results in diminished model performance. Significantly, the further improvement of OBIA annotations substantially boosts model performance metrics exemplified in the use of 100% composition in training data. On average, each model produces Pixel Accuracy of 0.942, IoUr of 0.012, mean IoU of 0.477, and Dice Score of 0.495 for every use of 100% OBIA annotations in the training data. Improvement in model performance evaluation metrics occurs when using 100% improved OBIA annotations in the training data where on average each model produces Pixel Accuracy values of 0.954, IoUr of 0.433, mean IoU of 0.692, and Dice Score of 0.771. The e

关键词： Annotations Roads Training data Data models image analysis Training Deep learning Pixel object-based image analysis deeplab v3+ pixel accuracy mean IoU

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Development of an object-based image analysis tool for mass spectrometry imaging ion classification

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2023年第19期415卷 4725-4730页

作者： Eisenberg, Seth M. Knizner, Kevan T. Muddiman, David C. North Carolina State Univ Dept Chem FTMS Lab Human Hlth Res Raleigh NC 27695 USA

Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.

关键词： IR-MALDESI object-based image analysis Mass spectrometry imaging image segmentation image classification MATLAB

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