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Identifying Generalizable image Segmentation Parameters for Urban Land Cover Mapping through Meta-analysis and Regression Tree Modeling

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REMOTE SENSING 2018年第1期10卷 73-73页

作者： Johnson, Brian A. Jozdani, Shahab E. Inst Global Environm Strategies Nat Resources & Ecosyst Serv Area 2108-11 Kamiyamaguchi Hayama Kanagawa 2400115 Japan Univ Isfahan Dept Geomat Engn Esfahan *** Iran

The advent of very high resolution (VHR) satellite imagery and the development of Geographic object-based image analysis (GEOBIA) have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS) algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters). The scale parameter (SP) is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific) could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water) through meta-analysis and nonlinear regression tree (RT) modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions), and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our approach over

关键词： high spatial resolution imagery multiresolution segmentation object-based image analysis GEOBIA

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Alternative stable states of tidal marsh vegetation patterns and channel complexity

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ECOHYDROLOGY 2016年第8期9卷 1639-1662页

作者： Moffett, K. B. Gorelick, S. M. Washington State Univ Sch Environm 14204 NE Salmon Creek Ave Vancouver WA 98686 USA Stanford Univ Dept Earth Syst Sci Stanford CA 94305 USA

Intertidal marshes develop between uplands and mudflats, and develop vegetation zonation, via biogeomorphic feedbacks. Is the spatial configuration of vegetation and channels also biogeomorphically organized at the intermediate, marsh-scale? We used high-resolution aerial photographs and a decision-tree procedure to categorize marsh vegetation patterns and channel geometries for 113 tidal marshes in San Francisco Bay estuary and assessed these patterns' relations to site characteristics. Interpretation was further informed by generalized linear mixed models using pattern-quantifying metrics from object-based image analysis to predict vegetation and channel pattern complexity. Vegetation pattern complexity was significantly related to marsh salinity but independent of marsh age and elevation. Channel complexity was significantly related to marsh age but independent of salinity and elevation. Vegetation pattern complexity and channel complexity were significantly related, forming two prevalent biogeomorphic states: complex versus simple vegetation-and-channel configurations. That this correspondence held across marsh ages (decades to millennia) and at both high and low marsh elevations suggests the following: (1) marshes of shared physiography can exhibit highly variable ecosystem structures;(2) young marshes are not necessarily simple nor necessarily develop vegetation complexity with age and elevation;(3) Bay marshes should continue to exhibit both simple/complex configurations in the future despite a likely shift toward low marshes;(4) salt marshes may tend to occupy two alternative stable states characterized by linked complexity in vegetation and channel organization. This final point may help fill the gap at the marsh scale between biogeomorphic models explaining marsh occurrence at larger coastal and smaller vegetation patch scales. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： tidal marsh estuary biogeomorphic vegetation zonation stable state San Francisco Bay object-based image analysis

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Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines

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JOURNAL OF CLEANER PRODUCTION 2019年 209卷 819-833页

作者： Johansen, Kasper Erskine, Peter D. McCabe, Matthew F. King Abdullah Univ Sci & Technol Water Desalinat & Reuse Ctr Al Jazri Bldg West Thuwal 239556900 Saudi Arabia Univ Queensland Sustainable Minerals Inst Ctr Mined Land Rehabil Brisbane Qld 4072 Australia

Mine sites are routinely required to rehabilitate their post-mining landforms with a safe, stable and sustainable land-cover. To assess these post-mining landforrns, traditional on-ground field monitoring is generally undertaken. However, these labour intensive and time-consuming measurements are generally insufficient to catalogue land rehabilitation efforts across the large scales typical of mining sites (>100 ha). As an alternative, information derived from Unmanned Aerial Vehicles (UAV) can be used to map rehabilitation success and provide evidence of achieving rehabilitation site requirements across a range of scales. UAV based sensors have the capacity to collect information on rehabilitation sites with extensive spatial coverage in a repeatable, flexible and cost-effective manner. Here, we present an approach to automatically map indicators of safety, stability and sustainability of rehabilitation efforts, and demonstrate this framework across three coalmine sites. Using multi-spectral UAV imagery together with geographic object-based image analysis, an empirical classification system is proposed to convert these indicators into a status category based on a number of criteria related to land-cover, landform, erosion, and vegetation structure. For this study, these criteria include: mapping tall trees (Eucalyptus species);vegetation extent;senescent vegetation;extent of bare ground;and steep slopes. Converting these land-cover indicators into appropriate mapping categories on a polygon basis indicated the level of rehabilitation success and how these varied across sites and age of the rehabilitation activity. This work presents a framework and workflow for undertaking a UAV based assessment of safety, stability and sustainability of mine rehabilitation and also provides a set of recommendations for future rehabilitation assessment efforts. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Open cut mine Rehabilitation Monitoring UAV object-based image analysis Indicators

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Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data

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ECOLOGICAL INDICATORS 2016年 64卷 49-58页

作者： Schafer, Elisa Heiskanen, Janne Heikinheimo, Vuokko Pellikka, Petri Univ Helsinki Dept Geosci & Geog POB 64 FI-00014 Helsinki Finland

With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing has much potential for biodiversity mapping, and high spatial resolution imaging spectroscopy (IS) allows for direct prediction of tree species diversity based on spectral reflectance. The objective of this study was to test an approach for mapping tree species alpha diversity that takes advantage of an unsupervised object-based clustering. Tree species diversity of a tropical montane forest in the Taita Hills, Kenya, was mapped based on spectral variation of high spatial resolution IS data. Airborne IS data and species data from 31 field plots were collected in the study area. Species diversity measures were obtained from the IS data by clustering spectrally similar image segments representing tree crowns. In order to do this, the image was segmented to objects that represented tree crowns. Three measures of species diversity were calculated based on the field data and on the clustering results, and the relationships were statistically analyzed. According to the results, the approach succeeded well in revealing tree species diversity patterns. Especially, tree species richness was well predicted (RMSE = 3 species;r(2) = 0.50) directly based on the clustering results. The optimal number of clusters was found to be close to the estimated number of tree species in the forest. Minimum tree size was an important determinant of the relationships, because only part of the trees are visible to the airborne sensor in the multi-layered closed canopy forest. In general, the object-based approach proved to be a viable alternative to a pixel-based clustering. The approach takes advantage of the capability of IS to detect spectral differences among tree crowns, but without the need for spectral training data, which is expensive to collect. With further development, the approach could be ap

关键词： Tree species diversity Imaging spectroscopy Clustering Tropical forest object-based image analysis

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Remote sensing of large wood in high-resolution satellite imagery: Design of an automated classification work-flow for multiple wood deposit types

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EARTH SURFACE PROCESSES AND LANDFORMS 2021年第12期46卷 2333-2348页

作者： Sendrowski, Alicia Wohl, Ellen Colorado State Univ Dept Geosci Ft Collins CO 80523 USA Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

Wood researchers increasingly rely on remote-sensing products to augment field information about wood deposits in river corridors. The availability of very high-resolution (<1 m) satellite imagery makes capturing wood over greater spatial extents possible, but previous studies have found difficulty in automatically extracting wood deposits due to the challenge in distinguishing wood from spectrally similar corridor features such as sand. We also lack knowledge on the spectral properties of different wood deposit types in multiple depositional environments. In this work, we explore image classification work-flows for four wood deposit types in three North American environments: in-channel jams deposited in the Tatshenshini River in Alaska, USA;a wood raft on the Slave River in Northwest Territories, Canada;and wood deposited along a lakeshore and coastal embayment in the Mackenzie River Delta in Northwest Territories, Canada. We compare classification results of object-based and pixel-based image analysis with supervised [support vector machine (SVM)] and unsupervised (ISO clustering) classifiers. We evaluate several accuracy assessment parameters and achieve overall classification accuracies of 65-99%, showing automated image classification is a possible approach for analysing wood across larger areas. We also find that wood sensitivity in the classification ranged from 0 to 95%, indicating that some techniques are better suited to wood capture than others. We find that supervised classification produced more accurate wood maps, though there is large variation in classification outcomes across environments related to spatial arrangement of wood in the landscape. We discuss the influence of depositional environment on classification and provide recommendations for designing a wood classification work-flow.

关键词： depositional environments large wood object-based image analysis remote sensing river delta

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Semantic Unsupervised Change Detection of Natural Land Cover With Multitemporal object-based analysis on SAR images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第7期59卷 5494-5514页

作者： Amitrano, Donato Guida, Raffaella Iervolino, Pasquale Univ Napoli Federico II Dept Elect & Informat Technol Engn I-80125 Naples Italy Univ Napoli Ingn Elettron & Telecomunicaz I-80125 Naples Italy Surrey Space Ctr Dept Elect Engn Guildford GU2 7XH Surrey England

Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11x0025;with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5x0025;. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios.

关键词： Vegetation mapping Biomass Semantics Synthetic aperture radar Agriculture image segmentation Radar polarimetry Change detection classification multitemporal synthetic aperture radar (SAR) object-based image analysis

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object-based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level

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REMOTE SENSING OF ENVIRONMENT 2014年 154卷 192-201页

作者： Voltersen, Michael Berger, Christian Hese, Soeren Schmullius, Christiane Univ Jena Inst Geog Dept Earth Observat D-07743 Jena Germany

Cities have evolved under manifold geographical, economical, historical, and cultural criteria, resulting in various sizes and shapes. Each city exhibits individual features and unique characteristics, despite that structural similarities appear. The separation into individual patterns, commonly named urban structure types (USTs), supports the characterization of physical, functional, and energetic factors of settlement structures, enabling associated environmental and socio-economic investigations as well as the comparison between the patterns of different cities. This study presents an automated approach for the classification of USTs based on remote sensing data in order to analyze the links between settlement structures and environmental issues, such as air pollution or urban heat islands, in a later stage of the project. Initially, an object-based classification routine is implemented to identify the land cover for the city of Berlin, utilizing spatially very high resolution aerial images and object height information. UST classes are defined based on the occurrence within the study area and are delimited by block boundaries. Afterwards, indicators for the derivation of USTs are generated based on the previously derived land cover information and the most valuable features are selected with the help of Random Forests. Finally, structural units are classified, involving common and new land cover based parameters. The focus is on the generation of an automated and transferable routine for a comprehensive UST classification covering the entire city. Comparing the results with reference data, good classification accuracies for both land cover and USTs indicate the suitability of the proposed method. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Urban areas object-based image analysis Airborne Land cover mapping Urban structure types

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Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite images

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REMOTE SENSING 2014年第4期6卷 2912-2939页

作者： Wieland, Marc Pittore, Massimiliano GFZ German Res Ctr Geosci Ctr Early Warning Sect Phys Earthquakes & Volcanoes 2 1 D-14473 Potsdam Germany

In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView-2, Quickbird, Ikonos) multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm's performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based), have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.

关键词： machine learning data mining urban remote sensing object-based image analysis

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Landslide mapping using a combination of sentinel-2 multi spectral instruments and GIS data at Namwon, Jeollabuk-do, South Korea

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GEOMECHANICS AND ENGINEERING 2024年第4期39卷 385-396页

作者： Lim, Hwan-Hui Lee, Seung-Min Cheon, Enok Song, Eu Jeon, Jun-Seo Lee, Seung-Rae K water Res Inst Water Infrastruct Res Ctr Daejeon 34141 South Korea Republ Korea Armed Forces 21st Infantry Div Seoul South Korea Natl Inst Forest Sci Dept Forest Environm & Conservat Landslide Team Seoul 02455 South Korea Korea Inst Civil Engn & Bldg Technol Dept Geotech Engn Res Goyang Si 10223 South Korea

With the recent development of satellite, aerial, and remote sensing technologies, it is easy to produce landslide inventory maps over a large area. In this study, the object-based framework was designed to address the limitations inherent in the pixel-based deep learning (DL) methodology. This framework explores the potential of combining Sentinel-2 MultiSpectral Instrument (MSI) satellite imagery and digital elevation models (DEMs) to enhance shallow landslide mapping across diverse terrains comprehensively. The study area for analysis and verification was selected as Jucheon-myeon, Namwon-si, and Jeollabuk-do, where significant large-scale landslides and slope failures occurred in 2020. As a result, the application of this framework led to the classification of 68 candidate sites spanning an area of 0.5 hectares or more. Site surveying was conducted on 20 random sites with a 1ha or more scale. Furthermore, six sites were selected where satellite imagery could discern the damaged areas. At these locations, the damaged area estimated by the framework was compared with the actual observed damaged area to assess accuracy. These rapid and cost-effective landslide mapping techniques can accurately estimate the location and extent of landslides and enhance the precision of sensitivity models and land management strategies.

关键词： image classification inventory map landslide object-based image analysis satellite

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Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery

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REMOTE SENSING OF ENVIRONMENT 2017年 196卷 193-204页

作者： Gudex-Cross, David Pontius, Jennifer Adams, Alison Univ Vermont Rubenstein Sch Environm & Nat Resources Aiken Ctr 81 Carrigan Dr Burlington VT 05405 USA

Spatially-explicit tree species distribution maps are increasingly valuable to forest managers and researchers in light of the effects of climate change and invasive pests on forest resources. Traditional forest classifications are limited to broad classes of forest types with variable accuracy. Advanced remote sensing techniques, such as spectral unmixing and object-based image analysis, offer novel forest mapping approaches by quantifying proportional species composition at the pixel level and utilizing ancillary environmental data for forest classifications. This is particularly useful in the Northeastern region of the United States where species composition is often mixed. Here we employed a hierarchical forest mapping approach using spectral unmixing of multi-temporal Landsat imagery to quantify percent basal area for ten common tree species/genera across northern New York and Vermont. Basal area maps were then refined using an object-based ruleset to produce a thematic forest classification. Validation with 50 field inventory plots covering a range of species compositions indicated that the quality of percent basal area mapping largely reflected the number of "pure" (>80% BA) endmember plots available for calibration, with more common species mapped at a higher accuracy (i.e. Acer saccharum, adj. r(2) = 0.44, compared to Populus sp., adj. r(2) = 0.24). The resulting thematic forest classification mapped 15 forest classes (nine species/genus level and six common species assemblages) with overall accuracy = 42%, KHAT = 33%, fuzzy accuracy = 86% at the pixel level, and 38%, KHAT = 29%, fuzzy accuracy = 84% at the object level. Using the validation plots to compare existing forest classification products, this hierarchical approach provided more class detail (11 represented classes) and higher accuracy than the National Forest Type Map (six represented classes, overall accuracy 18%, fuzzy accuracy 70%), LANDFIRE (five represented classes, overall accuracy 28%, fuz

关键词： Remote sensing Basal area mapping Biophysical modeling Hierarchical classification object-based image analysis

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