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An object-based classification Method to Detect Methane Ebullition Bubbles in Early Winter Lake Ice

REMOTE SENSING 2019年第7期11卷 822-822页

作者： Lindgren, Prajna Grosse, Guido Meyer, Franz J. Anthony, Katey Walter Univ Alaska Fairbanks Inst Geophys Fairbanks AK 99775 USA Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst D-14473 Potsdam Germany Univ Alaska Fairbanks Water & Environm Res Ctr Fairbanks AK 99775 USA

Thermokarst lakes in the Arctic and Subarctic release carbon from thawing permafrost in the form of methane and carbon dioxide with important implications for regional and global carbon cycles. Lake ice impedes the release of gas during the winter. For instance, bubbles released from lake sediments become trapped in downward growing lake ice, resulting in vertically-oriented bubble columns in the ice that are visible on the lake surface. We here describe a classification technique using an object-based image analysis (OBIA) framework to successfully map ebullition bubbles in airborne imagery of early winter ice on an interior Alaska thermokarst lake. Ebullition bubbles appear as white patches in high-resolution optical remote sensing images of snow-free lake ice acquired in early winter and, thus, can be mapped across whole lake areas. We used high-resolution (9-11 cm) aerial images acquired two and four days following freeze-up in the years 2011 and 2012, respectively. The design of multiresolution segmentation and region-specific classification rulesets allowed the identification of bubble features and separation from other confounding factors such as snow, submerged and floating vegetation, shadows, and open water. The OBIA technique had an accuracy of >95% for mapping ebullition bubble patches in early winter lake ice. Overall, we mapped 1195 and 1860 ebullition bubble patches in the 2011 and 2012 images, respectively. The percent surface area of lake ice covered with ebullition bubble patches for 2011 was 2.14% and for 2012 was 2.67%, representing a conservative whole lake estimate of bubble patches compared to ground surveys usually conducted on thicker ice 10 or more days after freeze-up. Our findings suggest that the information derived from high-resolution optical images of lake ice can supplement spatially limited field sampling methods to better estimate methane flux from individual lakes. The method can also be used to improve estimates of methane ebulli

关键词： methane ebullition mapping lake ice object-based image classification aerial photography thermokarst lake permafrost carbon feedback

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A hybrid OSVM-OCNN Method for Crop classification from Fine Spatial Resolution Remotely Sensed imagery

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REMOTE SENSING 2019年第20期11卷 2370-2370页

作者： Li, Huapeng Zhang, Ce Zhang, Shuqing Atkinson, Peter M. Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130000 Jilin Peoples R China Univ Lancaster Lancaster Environm Ctr Lancaster LA1 4YQ England Ctr Ecol & Hydrol Lib Ave Lancaster LA1 4AP England Univ Lancaster Fac Sci & Technol Lancaster LA1 4YR England

Accurate information on crop distribution is of great importance for a range of applications including crop yield estimation, greenhouse gas emission measurement and management policy formulation. Fine spatial resolution (FSR) remotely sensed imagery provides new opportunities for crop mapping at a detailed level. However, crop classification from FSR imagery is known to be challenging due to the great intra-class variability and low inter-class disparity in the data. In this research, a novel hybrid method (OSVM-OCNN) was proposed for crop classification from FSR imagery, which combines a shallow-structured object-based support vector machine (OSVM) with a deep-structured object-based convolutional neural network (OCNN). Unlike pixel-wise classification methods, the OSVM-OCNN method operates on objects as the basic units of analysis and, thus, classifies remotely sensed images at the object level. The proposed OSVM-OCNN harvests the complementary characteristics of the two sub-models, the OSVM with effective extraction of low-level within-object features and the OCNN with capture and utilization of high-level between-object information. By using a rule-based fusion strategy based primarily on the OCNN's prediction probability, the two sub-models were fused in a concise and effective manner. We investigated the effectiveness of the proposed method over two test sites (i.e., S1 and S2) that have distinctive and heterogeneous patterns of different crops in the Sacramento Valley, California, using FSR Synthetic Aperture Radar (SAR) and FSR multispectral data, respectively. Experimental results illustrated that the new proposed OSVM-OCNN approach increased markedly the classification accuracy for most of crop types in S1 and all crop types in S2, and it consistently achieved the most accurate accuracy in comparison with its two object-based sub-models (OSVM and OCNN) as well as the pixel-wise SVM (PSVM) and CNN (PCNN) methods. Our findings, thus, suggest that the propos

关键词： crop mapping object-based image classification deep learning decision fusion FSR remotely sensed imagery

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A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution image Analysis

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REMOTE SENSING 2018年第4期10卷 537-537页

作者： Baker, Fraser Smith, Claire L. Cavan, Gina Manchester Metropolitan Univ Sch Sci & Environm John Dalton BldgChester St Manchester M1 5GD Lancs England Univ Leicester Dept Geog Univ Rd Leicester LE1 7RH Leics England

Domestic gardens are an important component of cities, contributing significantly to urban green infrastructure (GI) and its associated ecosystem services. However, domestic gardens are incredibly heterogeneous which presents challenges for quantifying their GI contribution and associated benefits for sustainable urban development. This study applies an innovative methodology that combines citizen science data with high resolution image analysis to create a garden dataset in the case study city of Manchester, UK. An online Citizen Science Survey (CSS) collected estimates of proportional coverage for 10 garden land surface types from 1031 city residents. High resolution image analysis was conducted to validate the CSS estimates, and to classify 7 land surface cover categories for all garden parcels in the city. Validation of the CSS land surface estimations revealed a mean accuracy of 76.63% (s = 15.24%), demonstrating that citizens are able to provide valid estimates of garden surface coverage proportions. An object based image Analysis (OBIA) classification achieved an estimated overall accuracy of 82%, with further processing required to classify shadow objects. CSS land surface estimations were then extrapolated across the entire classification through calculation of within image class proportions, to provide the proportional coverage of 10 garden land surface types (buildings, hard impervious surfaces, hard pervious surfaces, bare soil, trees, shrubs, mown grass, rough grass, cultivated land, water) within every garden parcel in the city. The final dataset provides a better understanding of the composition of GI in domestic gardens and how this varies across the city. An average garden in Manchester has 50.23% GI, including trees (16.54%), mown grass (14.46%), shrubs (9.19%), cultivated land (7.62%), rough grass (1.97%) and water (0.45%). At the city scale, Manchester has 49.0% GI, and around one fifth (20.94%) of this GI is contained within domestic gardens. Th

关键词： urban gardens land surface cover citizen science land cover classification urban green space green infrastructure object-based image classification

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A Geographic object-based Approach for Land classification Using LiDAR Elevation and Intensity

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2017年第5期14卷 669-673页

作者： Zhou, Xiran Li, Wenwen Arizona State Univ Sch Geog Sci & Urban Planning Tempe AZ 85281 USA

By providing detailed height and intensity land surface information, high-resolution LiDAR data have proved to be effective in supporting land classification when combined with other major geospatial data sources, such as hyperspectral images. However, rectifying and fusing multisource geospatial data involves what normally is a manual and time-consuming process. In this letter, we propose a geographic object-based image analysis approach to enable semiautomatic land classification and mapping using LiDAR elevation and intensity data. The methodological framework consists of a series of operations, including preprocessing, object-based segmentation, creation of statistical variables from elevation and intensity, and semisupervised classification. We have successfully applied this approach to the classification of multiple land features, including asphalt, grass, barren land, swimming pool, shrubland, pavement, and buildings. Results show that our proposed approach performs better than LiDAR analysis methods in classifying different land parcels.

关键词： Geographic object-based image analysis (GEOBIA) LiDAR data classification object-based image classification

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Fuzzy Ontologies for Semantic Interpretation of Remotely Sensed images 21

Fuzzy Ontologies for Semantic Interpretation of Remotely Sen...

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Conference on image and Signal Processing for Remote Sensing XXI

作者： Khelifa, Djerriri Mimoun, Malki Ctr Spatial Tech Div Earth Observat Arzew 31200 Oran Algeria Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes 22000 Algeria

ISBN: (纸本)9781628418538

object-based image classification consists in the assignment of objects that share similar attributes to object categories. To perform such a task the remote sensing expert uses its personal knowledge, which is rarely formalized. Ontologies have been proposed as solution to represent domain knowledge agreed by domain experts in a formal and machine readable language. Classical ontology languages are not appropriate to deal with imprecision or vagueness in knowledge. Fortunately, Description Logics for the semantic web has been enhanced by various approaches to handle such knowledge. This paper presents the extension of the traditional ontology-based interpretation with fuzzy ontology of main land-cover classes in Landsat8-OLI scenes (vegetation, built-up areas, water bodies, shadow, clouds, forests) objects. A good interpretation of image objects was obtained and the results highlight the potential of the method to be replicated over time and space in the perspective of transferability of the procedure.

关键词： object-based image classification Landsat8-OLi imagery Fuzzy Ontology

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BENTHIC HABITAT MAPPING AND BIODIVERSITY ANALYSIS IN THE PRIMEIRAS AND SEGUNDAS ARCHIPELAGO RESERVE 36

BENTHIC HABITAT MAPPING AND BIODIVERSITY ANALYSIS IN THE PRI...

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36th International Symposium on Remote Sensing of Environment

作者： Teixeira, L. Nilsson, M. Hedley, J. Shapiro, A. Lund Univ Dept Phys Geog & Ecosyst Sci Solvegatan 12 S-22362 Lund Sweden Argans Ltd Plymouth Devon England Environm Comp Sci Ltd Tiverton Devon England World Wide Fund Nat WWF Germany Berlin Germany

ISBN: (纸本)9781629935201

The Primeiras and Segundas Archipelago Reserve is a recently established marine protected area, the largest in Africa, located in the waters of Northern Mozambique. This protected area is of significant local economic importance and global ecological relevance, containing the southernmost coral reefs in Eastern Africa. However, information related to the marine ecosystem, notably benthic habitat is very scarce. Twelve atolls were mapped in the region using object-based image classification of very-high resolution satellite imagery (IKONOS, Quickbird, and WorldView-2). Geographically referenced data on benthic cover and depth were gathered in the course of three fieldwork expeditions covering a total of four atolls and two shallow reef structures in the Segundas Archipelago. The resulting map allows the estimation of three distinct types of coral cover (field, patches, spurs and grooves);the differentiation of sand, rubble and rock substrate;and the detection of seagrass and brown macroalgae, identifying up to 24 benthic habitats. Average overall accuracy was above 50%. The high variability of the optical properties on the reef systems, in large due to the connectivity with the mainland via plumes, while interesting from an ecological perspective increases the challenges for remote sensing of bottom cover. New information indicates the presence of deep benthic cover extending from the atolls, suggesting the need for further research on Coastal Eastern African corals, namely on their resilience and connectivity, and supporting current knowledge of the existence of an almost continuous coral reef from Kenya to Mozambique. Coral and fish biodiversity data have been analysed together with the satellite-derived maps. Results support the local perception that ecosystems are in decline and uncover new information about biodiversity's spatial patterns. Our work provides a detailed depiction of marine habitats that may aid the management of the protected area, namely in the d

关键词： Very high resolution remote sensing object-based image classification Benthic habitats Coral reef Marine ecosystems Fish biodiversity Landscape metrics

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Savanna woody vegetation classification - now in 3-D

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APPLIED VEGETATION SCIENCE 2014年第1期17卷 172-184页

作者： Fisher, Jolene T. Erasmus, Barend F. N. Witkowski, Ed T. F. van Aardt, Jan Wessels, Konrad J. Asner, Gregory P. Univ Witwatersrand Restorat & Conservat Biol Res Grp Sch Anim Plant & Environm Sci Ctr African Ecol ZA-2050 Johannesburg South Africa Univ Witwatersrand Ctr African Ecol Sch Anim Plant & Environm Sci ZA-2050 Johannesburg South Africa Univ Witwatersrand Restorat & Conservat Biol Res Grp Sch Anim Plant & Environm Sci ZA-2050 Johannesburg South Africa Rochester Inst Technol Carlson Ctr Imaging Sci Rochester NY 14623 USA CSIR Meraka Inst Remote Sensing Res Unit ZA-0001 Pretoria South Africa Carnegie Inst Sci Dept Global Ecol Stanford CA 94305 USA

QuestionThe co-existence of woody plants and grasses characterize savannas, with the horizontal and vertical spatial arrangement of trees creating a heterogeneous biotic environment. To understand the influence of biogeophysical drivers on the spatial patterns of 3-D structure of woody vegetation, these patterns need to be explained over large areas to capture the context. Is there a spatially explicit, ecologically meaningful way to capture the patterns and context of 3-D woody vegetation structure? Locationclassification development and testing sites: landscapes in Bushbuckridge Municipality, Sabi Sand Wildtuin and Kruger National Park, Mpumalanga province, north-east South Africa. MethodsThe aforementioned structural classification approach requires appropriate 3-D and spatially explicit remote sensing data. A LiDAR-based canopy height model (CHM) and volumetric pixel (voxel) data from the Carnegie Airborne Observatory Alpha system were used to create the structural classification. First, we segmented the CHM images using multi-threshold and multi-resolution image segmentation techniques, and classified the image segments into four height classes, namely shrub (1-3m), low tree (3-6m), high tree (6-10m) or tall tree (>10m). A hierarchical a priori approach was used to develop classification criteria. The following metrics were calculated for 0.25-ha grid cells based on the cover and spatial arrangement of the four height classes: canopy cover, sub-canopy cover, canopy layers, Simpson's diversity index and cohesion. Top of canopy vegetation was classified using each metric at the 0.25-ha scale, with canopy cover being the primary classification metric. Subsequently, individual layers identified within the canopy were classified using the voxel data. We use a code system for describing classes to ensure standardization between different regions;a more traditional naming system may be used in addition for interpretation. ConclusionThis system provides a more comprehe

关键词： Carnegie Airborne Observatory Heterogeneity LiDAR object-based image classification Vegetation structure Woody vegetation

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Towards a vector agent modelling approach for remote sensing image classification

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JOURNAL OF SPATIAL SCIENCE 2014年第2期59卷 283-296页

作者： Borna, K. Moore, A. B. Sirguey, P. Univ Otago Sch Surveying Dunedin New Zealand

object-based remote sensing image classification is known for its ability to elicit objects that correspond one-on-one with real-world objects. However, it is still subject to a two-stage linear segmentation and classification process and a limited ability to use geometry, class identity and neighbourhood information in that process. This paper explores the scope of intelligent vector agents (VAs), potentially unifying segmentation and classification, and, as implemented through the Geographic Automata System framework, explicitly modelling a set of vector objects with (1) elastic geometry, (2) states, (3) neighbourhoods and embedded rules connecting all three. A brief illustration involving geometry, geometry rules and states is presented.

关键词： dynamic image object object-based image classification vector agent

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Unmanned aerial vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs

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APPLIED VEGETATION SCIENCE 2013年第3期16卷 509-517页

作者： Knoth, Christian Klein, Birte Prinz, Torsten Kleinebecker, Till Univ Munster Inst Landscape Ecol D-48149 Munster Germany Univ Munster Inst Geoinformat D-48151 Munster Germany

Question Can UAV-based NIR remote sensing support restoration monitoring of cut-over bogs by providing valid information on species distribution and surface structure? Location Restored polders of the Uchter Moor, a bog complex in NW Germany. Methods We used autonomously flying quadrocopters, supplied with either a panchromatic or colour infrared calibrated small frame digital camera to generate high resolution images of the restored bog surface. We performed a two-step classification process of automatic image segmentation and object-based classification to distinguish between four pre-defined classes (waterlogged bare peat, Sphagnum spp., Eriophorum vaginatum and Betula pubescens. An independent validation procedure was performed to evaluate the accuracy of the classification. Results A set-up composed of decision rules for reflectance, geometry and textural features was applied for identification of the four classes. The presented classification revealed an overall accuracy level of 91%. Most reliable attribution was obtained for waterlogged bare peat and Sphagnum-covered surfaces, revealing producer accuracies of 95% and 91%, respectively. Lower but still feasible accuracy levels were obtained for Eriophorum vaginatum and Betula pubescens individuals (89% and 84%, respectively). Conclusions UAV-based NIR remote sensing is a promising tool for monitoring the restoration of cut-over bogs and has the potential to significantly reduce laborious field surveys. UAVs may increasingly play a significant role in future ecological monitoring studies, since they are small in size, highly flexible, easy to handle, non-emissive and available at a comparatively low cost.

关键词： Bog vegetation Colour infrared Eriophorum Near-infrared object-based image classification Sphagnum UAV

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Very High Resolution Satellite image classification Using Fuzzy Rule-based Systems

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ALGORITHMS 2013年第4期6卷 762-781页

作者： Jabari, Shabnam Zhang, Yun Univ New Brunswick Dept Geodesy & Geomat Engn 15 Dineen Dr Fredericton NB E3B 5A3 Canada

The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process. In this study, an inclusive semi-automatic method for image classification is offered, which presents the configuration of the related fuzzy functions as well as fuzzy rules. The produced results are compared to the results of a normal classification using the same parameters, but with crisp rules. The overall accuracies and kappa coefficients of the presented method stand higher than the check projects.

关键词： fuzzy rule based systems object-based image classification very high resolution satellite imagery urban land cover

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