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A hybrid method combining SOM-based clustering and object-based analysis for identifying land in good agricultural condition

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2012年 83卷 92-101页

作者： Tasdemir, Kadim Milenov, Pavel Tapsall, Brooke Commiss European Communities Joint Res Ctr Monitoring Agr Resources Unit Inst Environm & Sustainabil I-21027 Ispra VA Italy

Remotely sensed imagery is currently used as an efficient tool for agricultural management and monitoring. In addition, the use of remotely sensed imagery in Europe has been extended towards determination of the areas potentially eligible for the farmer subsidies under the Common Agricultural Policy (CAP), through interactive or automatic land cover identification. For accurate quantification and fast identification of agricultural land cover areas from the imagery, a hybrid method, which combines automated clustering of self-organizing maps with object based image analysis, and called SOM + OBIA, is proposed. Performance analysis on three test zones (using multi-temporal Rapideye imagery) indicates that for the basic land cover categories (forest, water, vegetated areas, bare areas and sealed surfaces), unsupervised classification with the proposed SOM + OBIA method achieves an identification accuracy comparable to the accuracy of the traditional interactive object oriented analysis, with considerably less user interaction. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Land cover identification Unsupervised clustering Self-organizing maps CONN linkage object based image analysis Good agricultural condition

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Crop type detection using an object-based classification method and multi-temporal Landsat satellite images

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PADDY AND WATER ENVIRONMENT 2022年第3期20卷 395-412页

作者： Karimi, Neamat Sheshangosht, Sara Eftekhari, Mortaza Water Res Inst Dept Water Resources Study & Res Tehran Iran

Crop type detection is of great importance in water resource allocation and planning mostly in arid and semi-arid regions of Iran. Landsat-OLI 16-day inter-annual images are invaluable sources obviating crop monitoring into issues of crop types detection, crop yield prediction, and crop pattern studies. Although many classification methods such as decision tree (DT), support vector machine (SVM), and maximum likelihood (ML) were implied for crop type mapping, recent researches often use an object-based classification approach. In this study, an object-based image analysis (OBIA) classifier based on rule-based decision tree (RBDT) and object-based nearest neighbor (OBNN) used to delineate five common crop types (includes Wheat and Barley together in one class, rice, multiple crop (MC), Alfalfa and Spring crops) in Isfahan city and nearby areas. The classification was applied in five scenarios using different vegetation indexes including normalized difference vegetation index (NDVI), normalized difference water index (NDWI), green normalized difference vegetation index GNDVI and their combination. All scenarios property and accuracy assessed both with by class separation distance matrix and confusion matrix. The overall accuracy of classification with using only one vegetation index was lower than other scenarios. It was the lowest for GNDVI rating 37% whereas combination of Indexes resulted better accuracy. In final map with combination of NDVI, GNDVI and NDWI, overall accuracy and kappa achieved to 88% and 0/83 successively. Comparing individual accuracy of different crops showed that MC crops with 66% has the lowest accuracy and Wheat-Barely crops with 94.8% individual accuracy has the Maximum accuracy. Other crop types accuracy alters between 66 and 94.8%.

关键词： Crop type object based image analysis Segmentation NDVI NDWI

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Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2012年第1期67卷 134-147页

作者： Hoefle, Bernhard Hollaus, Markus Hagenauer, Julian Heidelberg Univ Inst Geog Chair GISci D-69120 Heidelberg Germany Vienna Univ Technol Inst Photogrammetry & Remote Sensing A-1040 Vienna Austria

This paper introduces a new GIS workflow for urban vegetation mapping from high-density (50 pts./m(2)) full-waveform airborne LiDAR data, combining the advantages of both raster and point cloud based analysis. Polygon segments derived by edge-based segmentation of the normalized digital surface model are used for classification. A rich set of segment features based on the point cloud and derived from full-waveform attributes is built, serving as input for a decision tree and artificial neural network (ANN) classifier. Exploratory data analysis and detailed investigation of the discriminative power of selected point cloud and full-waveform LiDAR observables indicate a high value of the occurrence of multiple distinct targets in a laser beam (i.e. 'echo ratio') for vegetation classification (98% correctness). The radiometric full-waveform observables (e.g. backscattering coefficient) do not suffice as single discriminators with low correctness values using a decision tree classifier (<= 72% correctness) but higher values with ANN (<= 95% correctness). Tests using reduced point densities indicate that the derived segment features and classification accuracies remain relatively stable even up to a reduction factor of 10 (5 pts./m(2)). In a representative study area in the City of Vienna/Austria the applicability of the developed object-based GIS workflow is demonstrated. The unique high density full-waveform LiDAR data open a new scale in 3D object characterization but demands for novel joint strategies in object-based raster and 3D point cloud analysis. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： Laser scanning LiDAR Calibration Vegetation object based image analysis Full-waveform

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Solid modeling of fossil small mammal teeth

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COMPUTERS & GEOSCIENCES 2011年第9期37卷 1364-1371页

作者： Marschallinger, Robert Hofmann, Peter Daxner-Hoeck, Gudrun Ketcham, Richard A. Austrian Acad Sci GISci Inst Salzburg Austria Nat Hist Museum Vienna Austria Univ Texas Austin Jackson Sch geosci High Resolut Xray CT Facil Austin TX 78712 USA

This paper presents an approach to create solid models of fossil small mammal teeth using a combination of microcomputed tomography, object based image analysis and voxel modeling. Small mammal teeth, because of their durability, are widely found in Cenozioc sediments the world over and play a key role in stratigraphy as well as in researching the rapid evolution and the paleogeographic spreading of small mammals. Recent advances in microcomputed tomography make this non-destructive analysis method an ideal data source for high-resolution 3D models of fossil small animal teeth. To derive internally consistent solid models of such fossils from micro-CT imagery, we propose a combination of 3D object based image analysis and solid modeling. Incorporating paleontological expert knowledge in the image processing cycle, object based image analysis yields topologically consistent image stacks classified by the main tooth components-enamel, dentine and pulp. Forwarding these data to a voxel modeling system, they can be quantitatively analyzed in an unprecedented manner: going beyond the possibilities of the state-of-art surface models, solid models are capable of unambiguously portraying the entire object volume-teeth can be peeled by material properties, subvolumes can be extracted and automatically analyzed by Boolean operations. The proposed method, which can be flexibly extended to handle a range of paleontological and geological micro-objects, is demonstrated with two typical fossil small mammal teeth. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Microcomputed tomography object based image analysis Voxel modeling

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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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Multitemporal change detection of urban trees using localized region-based active contours in VHR images

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REMOTE SENSING OF ENVIRONMENT 2012年 124卷 413-426页

作者： Ardila, Juan P. Biker, Wietske Tolpekin, Valentyn A. Stein, Alfred Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AA Enschede Netherlands

This study introduces active contours for multitemporal monitoring of urban trees using fine resolution imagery. In our implementation active contours locally identify tree crown image regions using a localized data energy term and competition of energy forces between adjacent contours. The simultaneous evolution of n contours represents an equivalent number of tree crowns which are initialized based on a fast and efficient image fitting procedure. We incorporate prior information into the multitemporal analysis, as optimized contours are propagated through a sequence of images. We demonstrate the applicability of the method to monitor individual trees and tree groups in two residential areas in the Netherlands using multispectral aerial images acquired over a period of five years. The method allowed the identification of abrupt and gradual tree crown changes in both areas and performed superior to an alternative region growing segmentation approach. We conclude that the method extracts meaningful image objects that facilitate tree crown monitoring and is effective to update tree crown spatial-databases in urban areas. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Tree detection Active contours Change detection Level sets object based image analysis Multitemporal analysis

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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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Mapping Green Spaces in Bishkek-How Reliable can Spatial analysis Be?

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REMOTE SENSING 2011年第6期3卷 1088-1103页

作者： Hofmann, Peter Strobl, Josef Nazarkulova, Ainura Austrian Acad Sci Dept GISci A-5020 Salzburg Austria Salzburg Univ Ctr Geoinformat A-5020 Salzburg Austria Austria Cent Asia Ctr GISci Bischkek 720020 Kyrgyzstan

Within urban areas, green spaces play a critically important role in the quality of life. They have remarkable impact on the local microclimate and the regional climate of the city. Quantifying the 'greenness' of urban areas allows comparing urban areas at several levels, as well as monitoring the evolution of green spaces in urban areas, thus serving as a tool for urban and developmental planning. Different categories of vegetation have different impacts on recreation potential and microclimate, as well as on the individual perception of green spaces. However, when quantifying the 'greenness' of urban areas the reliability of the underlying information is important in order to qualify analysis results. The reliability of geo-information derived from remote sensing data is usually assessed by ground truth validation or by comparison with other reference data. When applying methods of object based image analysis (OBIA) and fuzzy classification, the degrees of fuzzy membership per object in general describe to what degree an object fits (prototypical) class descriptions. Thus, analyzing the fuzzy membership degrees can contribute to the estimation of reliability and stability of classification results, even when no reference data are available. This paper presents an object based method using fuzzy class assignments to outline and classify three different classes of vegetation from GeoEye imagery. The classification result, its reliability and stability are evaluated using the reference-free parameters Best Classification Result and Classification Stability as introduced by Benz et al. in 2004 and implemented in the software package eCognition (***). To demonstrate the application potentials of results a scenario for quantifying urban 'greenness' is presented.

关键词： object based image analysis GeoEye urban green fuzzy classification classification reliability

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Detection of forest canopy gaps from very high resolution aerial images

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ECOLOGICAL INDICATORS 2018年第Part1期95卷 629-636页

作者： Nyamgeroh, Beryl B. Groen, Thomas A. Weir, Michael J. C. Dimov, Petar Zlatanov, Tzvetan POB 74130 Nairobi 00200 Kenya Univ Twente Fac Geoinformat Sci & Earth Observat ITC Enschede Netherlands Forest Res Inst 132 St Kl Ohridski Blvd Sofia 1756 Bulgaria Bulgarian Acad Sci Inst Biodivers & Ecosyst Res 2 Gagarin St BU-1113 Sofia Bulgaria

In Europe, assessment of the ecological function of forests is increasingly important as evidenced by Natura 2000, which has seen the establishment of protected sites that are of ecological importance. The presence of canopy gaps has proved to be a useful indicator of the forest structure and biodiversity in these areas. Two methods - object based image analysis (OBIA) and image Texture based analysis (ITBA) - were assessed to detect and quantify canopy gaps and to investigate the influence of forest type on their performance. The research was undertaken in two Natura 2000 sites in Bulgaria. To estimate the gap fractions in the field, 100 square plots (150 m by 150 m) were established. Within each plot, 25 circular sub-plots were laid out in a regular pattern. Gap size within each sub-plot was estimated visually. The methods were assessed using very high-resolution (0.13 m ground resolution) aerial imagery. For the OBIA method, five parameter settings were tested and the gap fractions estimated on the imagery and compared with the field estimates. Likewise, for the ITBA method, five different settings were tested. In general, both methods overestimated canopy gap fraction relative to the field-based estimates, especially where the gap fractions were relatively small. Larger gaps were more accurately estimated by the OBIA method. Neither method performed well in broadleaved forest. Statistical comparison showed that, overall, the correlations of the estimates from the imagery with field data are only moderate and that there is no significant difference between the two methods.

关键词： Gap detection object based image analysis Texture based analysis Forest structure Rhodopi Mountains Western Balkan Range

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Identifying urban built-up areas using CORONA imagery in the pre-Landsat era: a case study of Shanghai in 1965

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第6期45卷 2028-2048页

作者： Li, Shuang Fudan Univ Ctr Hist Geog Studies Shanghai Peoples R China Fudan Univ Ctr Hist Geog Studies 220 Handan Rd Shanghai 200433 Peoples R China

The Corona project (1959-1972) created a large number of high-resolution images that provided valuable historical research data before the advent of Landsat (1972) and even compensated for the lack of accuracy in early stages. In this study, we collected eight available Corona images to create a historical remote sensing image of Shanghai in the 1960 and developed an automatic image segmentation and classification methodology. In the experimental part, we verified its accuracy, and discussed the urbanization process in Shanghai. The results showed that 1) In 1965, the urban area of Shanghai was mainly distributed in Puxi, and the urbanization degree of the entire hinterland of Pudong was still very low. Built-up areas were highly concentrated in the main urban areas. 2) The overall accuracy was 91.5% and the kappa coefficient was 0.762. 3) We compared the built-up area in 1965 with the current urban area, which increased from 306.23 to 2,804.25 km2. Besides, historical imagery also captured the state of environmental pollution at that time. Overall, our method could inform the mapping and classification of remotely sensed land surfaces on a global scale in the 1960s, extending pre-Landsat era research to studies of urban history, archaeology and cultural heritage.

关键词： Corona pre-Landsat object based image analysis archaeology Keyhole

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