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Evaluation of semivariogram features for objectbased image classification

Geo-Spatial Information Science 2015年第4期18卷 159-170页

作者： Xian WU Jianwei PENG Jie SHAN Weihong CUI School of Remote Sensing and Information Engineering Wuhan University129 Luoyu RoadWuhan 430079China Central Southern China Electronic Power Design Institute of China Power Engineering Consulting Group 668 Minzhu RoadWuhan 430071China Lyles School of Civil Engineering Purdue UniversityWest LafayetteIN 47907USA Collaborative Innovation Center of Geospatial Technology Wuhan University129 Luoyu RoadWuhan 430079China

Inclusion of textures in image classification has been shown *** paper studies an efficient use of semivariogram features for object-based high-resolution image ***,an input image is divided into segments,for each of which a semivariogram is then ***,candidate features are extracted as a number of key locations of the semivariogram *** we use an improved Relief algorithm and the principal component analysis to select independent and significant *** the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine *** effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral *** described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.

关键词： object based image analysis image segmentation image classification texture feature semivariogram

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Detection and classification of mesoscale atmospheric phenomena above sea in SAR imagery

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REMOTE SENSING OF ENVIRONMENT 2015年 160卷 263-272页

作者： Topouzelis, Konstantinos Kitsiou, Dimitra Univ Aegean Sch Environm Dept Marine Sci Mitilini 81100 Greece

Satellite radar observations above sea are dedicated to small scale and mesoscale phenomena in the ocean and the marine boundary layer. What the Synthetic Aperture Radar (SAR) sensors see on the ocean surface is primarily the variation of the Bragg resonant waves providing signatures of local wind changes and imprints of atmospheric phenomena, and the areas of enhanced wave breaking associated with wave-current interaction for oceanographic processes like currents, fronts and eddies. SAR images contain information about the processes in the marine-atmosphere boundary layer (MABL), in particular when variation of wind speed and direction is significant. Several publications are available in the literature modeling and explaining each phenomenon separately. However, only a few focus on the identification of each phenomenon in an automatic or semi-automatic way. The objective of the present paper is to detect and to classify five atmospheric phenomena in SAR images: low wind areas;katabatic wind, island wakes, atmospheric gravity waves and atmospheric convective cells. An object-based environment was used to identify the phenomena and fuzzy logic used to classify them. Several features extracted for each phenomenon were used for feeding the classifier. The proposed method used 16 SAR images and accuracy was examined in another 16 SAR images using 457 object samples. The method proved to be successful with an overall accuracy of 80% and Kappa index of 0.75. The class of low wind areas negatively affected the overall accuracy due to overlapping feature values with the rest of the categories. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Oceanography ENVISAT object based image analysis Fuzzy classification Aegean Sea

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Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia

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WETLANDS ECOLOGY AND MANAGEMENT 2015年第6期23卷 1049-1066页

作者： Aziz, Ammar Abdul Phinn, Stuart Dargusch, Paul Omar, Hamdan Arjasakusuma, Sanjiwana Univ Queensland Sch Geog Planning & Environm Management Brisbane Qld 4072 Australia Forest Res Inst Malaysia Kepong 52109 Selangor Darul Malaysia

This paper explores the suitability of remotely sensed data obtained from Landsat imaging sensors, in combination with ancillary forest inventory data, for the ecological monitoring and management of a production mangrove forest in Malaysia. An assessment is presented on the capabilities and limitations of utilizing Landsat Thematic Mapper data for mapping mangrove species and stand age structure in the Matang Mangrove Forest Reserve. The cloud-free subset satellite images used in this study were acquired in May 1988, February 1995, December 1999, March 2001, December 2009 and November 2013. The December 1999 Landsat 7 ETM+ image was used for the error and accuracy assessment because it coincided with the year the reference map was produced. The image classification process included both pixel-based and object-based approaches conducted on a combination of software which included ENVI, ArcGIS 10.1 and eCognition. Results revealed that the pixel-based approach was not effective in delineating species and stand age structure due to the presence of the 'salt and pepper effect' in the image output. The object-based approach accurately delineated between commercial and non-commercial mangrove species. It also produced a reliable and accurate stand-age structure map based on three designated classes: clear-felled areas;young mangrove forest (less than 13 years old);and mature mangrove forest (13 years or older). The analysis highlights that the mapping and analysis of mangrove species and stand age structure with Landsat Thematic Mapper image datasets can play a useful role in achieving cost-efficient and ecologically sustainable outcomes in a production mangrove forest.

关键词： Landsat Species delineation Stand age delineation object based image analysis Matang mangrove forest reserve

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Observing morphological changes in natural land form through archived satellite images

Observing morphological changes in natural land form through...

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International Conference on Technologies for Sustainable Development (ICTSD 2015)

作者： Mendiratta, Priya Gedam, Shirish Indian Inst Technol Ctr Studies Resources Engn Bombay Maharashtra India

ISBN: (纸本)9781479981878

The Mumbai Metropolitan Region (MMR), located to the east of the Arabian Sea, experiences tropical climate with annual rainfall of over 250 cms. Geographically, the Thane Creek separates the island city from the mainland comprising low-lying marshy lands, and is lined by north-south trending hills on either side. The Creek has historically served as the natural course for rainwater discharge from the adjoining hilly terrain. Owing to its peculiar topography, the city has a distinct linear morphology wherein the habitable area along the Creek lies between the water front and these hills. This has necessitated that creation of new firm land on both its shores would either require cutting of foothills and/ or filling of erstwhile low lying marshy lands, both having fragile ecology of their own. This study highlights the significance of satellite image classification and change detection to observe the effect of rapid development and expansion of urban areas over time in the MMR on the morphology of the Creek and its adjoining wetlands. Change-detection analysis performed on classified time-series Landsat images, using object based image analysis (OBIA), reveal gradual change in the profile of the Thane Creek over the years. It is observed that the Creek width has reduced on the western shore, due to the siltation caused by anthropogenic intervention, thereby affecting the adjoining wetland ecosystems. The observations provide useful inputs for the stakeholders, to foster a comprehensive action plan to ensure sustainable futures for both mankind and biodiversity.

关键词： Landsat images object based image analysis Change Detection Thane Creek profile

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Measuring rural settlement expansion in Uzbekistan using remote sensing to support spatial planning

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APPLIED GEOGRAPHY 2015年 62卷 29-43页

作者： Conrad, C. Rudloff, M. Abdullaev, I. Thiel, M. Loew, F. Lamers, J. P. A. Univ Wurzburg Inst Geog Remote Sensing Unit Oswald Kulpe Weg 86 D-97074 Wurzburg Germany Reg Environm Ctr Cent Asia CAREC Alma Ata 050043 Kazakhstan Univ Bonn ZEF Ctr Dev Res D-53113 Bonn Germany

Population growth demands sustainable spatial planning strategies for settlements in Uzbekistan, Central Asia, especially in rural areas that are inhabited by approximately 64 percent of the country's population. Where can settlements expand in rural Uzbekistan and does settlement growth affect valuable agricultural land? SPOT-5 data with a resolution of 2.5 m was utilized for mapping building layers and assessing settlement growth between 2006 and 2011 at the example of 53 communities located in the Khorezm province in North-West Uzbekistan. object based image analysis was conducted, i.e. a multi-scale segmentation for the derivation of building contours, followed by a random forest (RF) classification of the object's spectral and spatial characteristics. A geographical information system (GIS) was used for identifying settlement densification and expansion processes, and for quantifying agricultural land parcels of different soil quality occupied during settlement growth. A calibration routine based on indices of segmentation quality enabled the selection of optimal segmentation parameters. After GIS-based refinements of the RF classification results, the overall accuracy (OA) of the building layers of both years exceeded 95%. The OA of the change map was 92.7%. The results revealed that the building area increased by 20%, whilst settlement expansion amounted to 10% in 2006 - 2011. Settlements widely expanded in accordance with the existing rules prohibiting the conversion of agricultural land to housing areas. Nevertheless, about 20% of the settlement growth occurred on agricultural production areas, also on those with highly productive soils. The results indicated both, the pressure on land resources for settlement growth and - in face of continuous population growth - an increasing demand for comprehensive spatial planning in rural Uzbekistan. The elaborated methodological approach can be extrapolated to regions throughout Central Asia with similar environmenta

关键词： Central Asia Rural settlement growth Remote sensing SPOT object based image analysis Spatial planning

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CHANGES IN THE LAND COVER AND LAND USE OF THE ITACAIUNAS RIVER WATERSHED, ARC OF DEFORESTATION, CARAJAS, SOUTHEASTERN AMAZON 36

CHANGES IN THE LAND COVER AND LAND USE OF THE ITACAIUNAS RIV...

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36th International Symposium on Remote Sensing of Environment

作者： Souza-Filho, P. W. M. Nascimento, W. R., Jr. Versiani de Mendonca, B. R. Silva, R. O., Jr. Guimaraes, J. T. F. Dall'Agnol, R. Siqueira, J. O. ITV Belem Para Brazil Vale SA Intelligence Planning & Environm Nova Lima MG Brazil Fed Univ Para Inst Geosci Belem Para Brazil

ISBN: (纸本)9781629935201

Human actions are changing the Amazon's landscape by clearing tropical forest and replacing it mainly by pasturelands. The focus of this work is to assess the changes in the Itacaiunas River watershed;an area located in the southeastern Amazon region, near Carajas, one of the largest mining provinces of the World. We used a Landsat imagery dataset to map and detect land covers ( forest and montane savanna) and land use ( pasturelands, mining and urban) changes from 1984 to 2013. We employed standard image processing techniques in conjunction with visual interpretation and geographic object-based classification. Land covers and land use (LCLU) "from-to" change detection approach was carried out to recognize the trajectories of LCLU classes based on object change detection analysis. We observed that similar to 47% (similar to 1.9 million ha) of forest kept unchanged;almost 41% (similar to 1.7 million ha) of changes was associated to conversion from forest to pasture, while 8% (similar to 333,000 ha) remained unchanged pasture. The conversion of forest and montane savannah to mining area represents only 0.24% (similar to 9,000 ha). We can conclude that synergy of visual interpretation to discriminate fine level objects with low contrast associated to urban, mining and savanna classes;and automatic classification of coarse level objects related to forest and pastureland classes is most successfully than use these methods individually. In essence, this approach combines the advantages of the human quality interpretation and quantitative computing capacity.

关键词： Landsat change detection object based image analysis GEOBIA multitemporal image analysis

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Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar imageries

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Procedia Environmental Sciences 2015年 24卷 215-221页

作者： Romie Jhonnerie Vincentius P. Siregar Bisman Nababan Lilik Budi Prasetyo Sam Wouthuyzen Marine Technology Graduate Program Faculty of Fisheries and Marine Science Bogor Agricultural University Bogor Indonesia Departmen of Aquatic Resources Utilization Faculty of Fisheries and Marine Science Riau University. Indonesia Department of Marine Science and Technology Faculty of Fisheries and Marine Science Bogor Agricultural University Bogor Indonesia Departmen of Forest Resources Conservation and Ecotourism Faculty of Forestry Bogor Agricultural University Bogor Indonesia Research Center for Oceanography Indonesian Institute of Science Jl. Pasir Putih I Ancol Timur Jakarta Indonesia

The objective of this research was to evaluate the accuracy of random forest classification rule using object based image analysis (OBIA) application (eCognition Developer) and the results were compared with common pixel-based classification algorithm (maximum likelihood/ML) for mangrove land cover mapping in Kembung River, Bengkalis Island, Indonesia. Seven data input model derived from Landsat 5TM bands, ALOS PALSAR FBD, and spectral transformations (NDVI, NDWI, NDBI) were examined by both classifiers. Feature objects statistical parameters were selected and implemented on random forest classifier. Overall accuracy (OA) as well as user and producer accuracies and Kappa statistic were used to compare classification results. Our results showed that the more data model used produced higher overall accuracy and kappa statistics for RF classifier. For each data input model, random forest classifier has higher overall accuracy than maximum likelihood. The best mangrove discrimination in RF classifier was achieved when the combination of Landsat 5 TM, SAR, and spectral transformation were used, while in ML classifier, the best mangrove discrimination was achieved when the combination of Landsat 5 TM and ALOS PALSAR was used. The overall accuracy achieved by RF classifier was 81.1% and 0.76 for Kappa statistic. Meanwhile, for ML classifier, the overall accuracy achieved was 77.7% and 0.71 for Kappa statistic.

关键词： Random forest classification object based image analysis pixel based classification Landsat TM ALOS PALSAR mangrove Kembung River Bengkalis island

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Region-shrinking: A hybrid segmentation technique for isolating continuous features, the case of oceanic eddy detection

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REMOTE SENSING OF ENVIRONMENT 2014年第0期153卷 90-98页

作者： Wu, Qingling Clark Univ Grad Sch Geog Worcester MA 01610 USA

In remote sensing image analysis, limited attention has been devoted to the isolation of fuzzy objects, namely those with inherently indeterminate boundaries, from continuous field data. This study develops a "region-shrinking" segmentation technique tailored specifically to the problem of fuzzy object identification, and applies it to the test case of oceanic eddy detection. The region-shrinking technique employs an evolutionary boundary definition procedure to simultaneously identify segments containing oceanic eddies, and refine the boundaries of these segments to correspond to eddy perimeters. This algorithm combines a Non-Euclidean Voronoi segmentation technique with insights from existing work on eddy detection from an oceanographic perspective, with the goal of improving the detection of eddy features through sea level anomaly imagery. It takes into account multiple criteria (vorticity, size, amplitude, the existence of local sea level extrema, concave or convex shape) to iteratively refine the identification and demarcation of oceanic eddies. The resulting polygons define tightly fitted eddy boundaries, and conform to the key rotational and cross-sectional height profile characteristics used by physical oceanographers to identify eddies. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Segmentation object based image analysis Fuzzy objects Oceanic eddies Okubo-Weiss parameter Sea level anomaly AVISO

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A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2014年第Aug.期94卷 13-24页

作者： Yang, Jian Li, Peijun He, Yuhong Univ Toronto Dept Geog Toronto ON M5S 3G3 Canada Peking Univ Sch Earth & Space Sci Inst Remote Sensing & GIS Beijing 100871 Peoples R China Univ Toronto Dept Geog Mississauga ON L5L 1C6 Canada

image segmentation is one of key steps in object based image analysis of very high resolution images. Selecting the appropriate scale parameter becomes a particularly important task in image segmentation. In this study, an unsupervised multi-band approach is proposed for scale parameter selection in the multi-scale image segmentation process, which uses spectral angle to measure the spectral homogeneity of segments. With the increasing scale parameter, spectral homogeneity of segments decreases until they match the objects in the real world. The index of spectral homogeneity is thus used to determine multiple appropriate scale parameters. The performance of the proposed method is compared to a single-band based method through qualitative visual interpretation and quantitative discrepancy measures. Both methods are applied for segmenting two images: a QuickBird scene of an urban area within Beijing, China and a Woldview-2 scene of a suburban area in Kashiwa, Japan. The proposed multi-band based segmentation scale parameter selection method outperforms the single-band based method with the better recognition for diverse land cover objects in different urban landscapes. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： object based image analysis Multi-scale segmentation Appropriate scale parameter selection Multi-band Spectral angle Segmentation evaluation

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object-oriented mapping of urban trees using Random Forest classifiers

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2014年第1期26卷 235-245页

作者： Puissant, Anne Rougier, Simon Stumpf, Andre Univ Strasbourg CNRS UMR 7362 Lab Image Strasbourg France Univ Strasbourg CNRS UMR 7516 Inst Phys Globe Strasbourg Strasbourg France

Since vegetation in urban areas delivers crucial ecological services as a support to human well-being and to the urban population in general, its monitoring is a major issue for urban planners. Mapping and monitoring the changes in urban green spaces are important tasks because of their functions such as the management of air, climate and water quality, the reduction of noise, the protection of species and the development of recreational activities. In this context, the objective of this work is to propose a methodology to inventory and map the urban tree spaces from a mono-temporal very high resolution (VHR) optical image using a Random Forest classifier in combination with object-oriented approaches. The methodology is developed and its performance is evaluated on a dataset of the city of Strasbourg (France) for different categories of built-up areas. The results indicate a good accuracy and a high robustness for the classification of the green elements in terms of user and producer accuracies. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Urban trees VHR satellite images object based image analysis Feature selection, Random Forest classifier

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