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Monitoring land use changes associated with urbanization: An object based image analysis approach

EUROPEAN JOURNAL OF REMOTE SENSING 2015年 48卷 85-99页

作者： Samal, Dipak R. Gedam, Shirish S. Indian Inst Technol Ctr Studies Resources Engn Bombay 400076 Maharashtra India

Land use/land cover (LULC) change occurs due to natural and anthropogenic causes. In developing countries, rapid industrialization and urbanization imposes a major threat to natural environment. Present study was carried out to monitor the LULC changes due to urbanization in a rapidly changing river basin, India. The purpose of choosing the river basin was to analyze past changes and predict possible consequences within a defined natural boundary. Multi-temporal images acquired from Landsat and Indian Remote Sensing (IRS) satellites between 1992-2009 as well as a digital elevation model was used to generate historical and current LULC pattern in the basin. An object based image analysis technique was employed for precise classification of multi-temporal images followed by GIS-based change detection studies. The study reveals that the built up area has increased significantly and added 288 km(2) between 1992 - 2009. Increase in built up area is attributed to decrease in wastelands and agricultural land. The expansion of built up area along major transportation networks, specifically after the year 2000 shows the rapid rate of urbanization in the basin.

关键词： GIS Upper Bhima basin urbanization object based image analysis change matrix

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A REGION-based MULTI-SCALE APPROACH FOR object-based image analysis 23

A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE A...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Kavzoglu, T. Erdemir, M. Yildiz Tonbul, H. Gebze Tech Univ Geomat Engn Dept TR-41400 Gebze Turkey

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

关键词： object based image analysis Region-based scale Classification Multiresolution Segmentation Estimation of Scale Parameter (ESP)

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Automatic counting of fission tracks using object-based image analysis for dating applications

Automatic counting of fission tracks using object-based imag...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lippold, J. Marpu, P. R. Gloaguen, R. Jonckheere, R. Tech Univ Bergakad Freiberg D-09596 Freiberg Germany

ISBN: (纸本)9781424412112

Geochronological dating with the fission-track method is based on time-consuming counts of the spontaneous and induced tracks. Automatic extraction and identification of tracks would not only improve the speed of track counting but also eliminate variation due to the observer. Pixel values alone are not enough to distinguish between tracks and background. Traditional pixel-based approaches are therefore inefficient for fission track counting. image analysis based on objects, which include shape, texture and context information is a more promising method.

关键词： object based image analysis fission-track-dating

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Monitoring Post Disturbance Forest Regeneration with Hierarchical object-based image analysis

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FORESTS 2013年第4期4卷 808-829页

作者： Moskal, L. Monika Jakubauskas, Mark E. Univ Washington Remote Sensing & Geospatial Anal Lab Sch Environm & Forest Sci Coll Environm Seattle WA 98195 USA Univ Kansas Kansas Appl Remote Sensing Program Lawrence KS 66047 USA

The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT) in image segmentation, and to apply the object-based image analysis (OBIA) approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial) imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.

关键词： seedling regeneration object based image analysis hierarchical classification

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Detection of forest canopy gaps from very high resolution aerial images

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ECOLOGICAL INDICATORS 2018年 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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object recognition in hyperspectral images using Binary Partition Tree representation

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PATTERN RECOGNITION LETTERS 2015年 56卷 45-51页

作者： Valero, Silvia Salembier, Philippe Chanussot, Jocelyn Univ Toulouse CESBIO CNES CNRS UMR 5126 IRD Toulouse France Tech Univ Catalonia UPC Barcelona Catalonia Spain Univ Iceland Fac Elect & Comp Engn Reykjavik Iceland Grenoble Inst Technolo4 GIPSA Lab Signal & Image Dept Grenoble France

In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： object based image analysis Binary Partition Tree Hyperspectral images object recognition

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Multi-scale RoIs selection for classifying multi-spectral images

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2020年第2期31卷 745-769页

作者： Seal, Ayan Garcia-Pedrero, Angel Bhattacharjee, Debotosh Nasipuri, Mita Lillo-Saavedra, Mario Menasalvas, Ernestina Gonzalo-Martin, Consuleo PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 India Univ Politecn Madrid Ctr Biomed Technol Campus Montegancedo Pozuelo De Alarcon 28233 Spain Jadavpur Univ Kolkata India Univ Concepcion Concepcion Chile

The applications of object-based image analysis (OBIA) in remote sensing studies have received a considerable amount of attention over the recent decade due to dramatically increasing of the spatial resolution of satellite imaging sensors for earth observation. In this study, an unsupervised methodology based on OBIA paradigm for the estimation of multi-scale training sets for land cover classification is proposed. The proposed method consists of selection of valid region of interests in an unsupervised way and its characterization using some attributes in order to form meaningful and reliable training sets for supervised classification of different land covers of a satellite image. Multi-scale image segmentation is a prerequisite step for estimation of multi-scale training sets. However, scale selection remains a challenge in multi-scale segmentation. In this work, we propose a method to determine the appropriate segmentation scale for each land cover with the help of prior knowledge in the form of in-situ data. The proposed method is further discussed and validated through multi-scale segmentation using quick shift and random forest algorithms on two multi-spectral images captured using Worldview-2 sensor. Experimental results indicate that the proposed method qualitatively and quantitatively outperforms three state-of-the-art methods.

关键词： object based image analysis Multi-scale RoIs Quick shift Random forest

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Evaluation of semivariogram features for objectbased image classification

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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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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 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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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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