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Process-based image analysis for agricultural mapping: A case study in Turkgeldi region, Turkey

ADVANCES IN SPACE RESEARCH 2015年第8期56卷 1635-1644页

作者： Avci, Z. Damla Uca Sunar, Filiz Istanbul Tech Univ Dept Astronaut Fac Aeronaut & Astronaut TR-34469 Istanbul Turkey Istanbul Tech Univ Dept Geomat Fac Civil Engn TR-34469 Istanbul Turkey

The need for timely, accurate, and interoperable geospatial information is steadily increasing. In this context, process-based image processing systems will be the initial segment for future's automatic systems. A process-based system is believed to be a good approach for agricultural purpose because agricultural activities are carried out according to a periodic (annual) cycle. Therefore, a process-based image analysis procedure was designed for routine crop classification for an agricultural region in Kirklareli, Turkey. The process tree developed uses a multi-temporal image data set as an input and gives the final crop classification as an output by using an incremental rule set. The test data set was composed of five images of Satellite Pour l'Observation de la Terre 4 (SPOT 4) data acquired in 2007. Basically, image objects were first extracted and then classified. A rule set was structured depending on class definitions. A decision-based process was executed and formed a multilevel image classification system. The final classification was obtained by merging classes from the appropriate levels where they were extracted. To evaluate the success of the application the accuracy of the classification was assessed. The overall accuracy and kappa index of agreement was found to be 80% and 0.78, respectively. At the end of the study, problems of segmentation and classification operations were discussed and solution approaches were outlined. To assess the process in terms of its scope for automation, the efficiency and success of the rule set were also discussed. (C) 2015 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Crop mapping object-based classification Image processing

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On the ensemble of multiscale object-based classifiers for aerial images: a comparative study

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第19期77卷 24565-24592页

作者： Esmael, Agnaldo Aparecido dos Santos, Jefersson Alex Torres, Ricardo da Silva Univ Estadual Campinas Inst Comp Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil Univ Fed Minas Gerais Dept Comp Sci Av Antonio Carlos 6627 Predio ICEx BR-31270010 Pampulha Belo Horizonte MG Brazil

Remote sensing images (RSIs) are increasingly used as data source to produce maps used in several applications. Modern sensors launched into space from the end of the 1990s have been producing high spatial resolution RSIs. The use of classification methods based on regions, called as Geographic object-based Image Analysis (GEOBIA), has been demonstrated to be more appropriate to deal with this kind of image. However, finding the appropriate segmentation scale, which is not a trivial task, is crucial for the success of a GEOBIA method. In this paper, we perform a comparative study involving seven methods for RSI multiclass classification that combine different features extracted from different scales: M1-OvA, M2-OvO, M3-AdaMH, M4-Samme, M5-MV, M5-WMV, and M6-Cascade. The first four methods are boosting-based techniques and the last three are based on the majority vote approach. The effectiveness of the proposed methods was evaluated by analyzing the results of experiments conducted in three RSIs datasets. The methods were compared with the baseline SVM with Kernel RBF by measuring the overall accuracy, the Kappa Index, and the accuracy per class. The results show that all the proposed methods are effective for RSI classification.

关键词： object-based classification Multiscale segmentation Multiclass classification Multiscale Classifier ensemble Comparative study Boosting Majority voting

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SVM-based classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

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REMOTE SENSING 2013年第8期5卷 3749-3775页

作者： Zhang, Jixian Lin, Xiangguo Ning, Xiaogang Chinese Acad Surveying & Mapping Key Lab Mapping Space Beijing 100830 Peoples R China

object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.

关键词： airborne LiDAR object-based classification point clouds segmentation SVM

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Scale parameter optimization through high-resolution imagery to support mine rehabilitated vegetation classification

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ECOLOGICAL ENGINEERING 2016年 97卷 130-137页

作者： Bao, Ni-sha Wu, Li-xin Liu, Shan-jun Li, Na Northeastern Univ Inst Geoinformat & Digital Mine 3rd Wenhua Rd Shenyang 110819 Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Peoples R China

Determining the appropriate scale of image objects is a critical aspect that affects segmentation quality in object-based classification. This research aims to assess the influence of three objective segmentation optimization methods for object-based vegetation classification through high-resolution imagery at mine rehabilitation site. These methods included estimation of scale parameter, segmentation error index, and Euclidean distance 2 index, which were employed to determine segmentation parameters, final classification accuracy, and optimal scale parameters for segmentation. The results showed that segmentation optimization may increase the classification accuracy of object-based analysis to extract information from artificial objects with regular shape, such as rehabilitated vegetation and urban green space. Given the high-resolution image with multi-spectral bands, the segmentation error and Euclidean distance 2 indices improved final classification accuracy relative to the control. Final classification accuracy obtained from each of four workflows further indicated that relatively small differences in scale parameters can lead to considerable differences in final classification accuracy. These methods automate the mail classification steps and improve the classification accuracy, only some parts of the classification require manual intervention, resulting in a more transferable approach with potentially less time required for classifying new imagery. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Mine rehabilitation object-based classification Segmentation Optimal scale High-resolution imagery

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Texture and Scale in object-based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2009年第3期47卷 761-770页

作者： Laliberte, Andrea S. Rango, Albert New Mexico State Univ USDA ARS Jornada Expt Range Las Cruces NM 88003 USA

Imagery acquired with unmanned aerial vehicles (UAVs) has great potential for incorporation into natural resource monitoring protocols due to their ability to be deployed quickly and repeatedly and to fly at low altitudes. While the imagery may have high spatial resolution, the spectral resolution is low when lightweight off-the-shelf digital cameras are used, and the inclusion or texture measures can potentially increase the classification accuracy. Texture measures have been used widely in pixel-based image analysis, but their use in an object-based environment has not been well documented. Our objectives were to determine the most suitable texture measures and the optimal image analysis scale for differentiating rangeland vegetation using UAV imagery segmented at multiple scales. A decision tree was used to determine the optimal texture features for each segmentation scale. Results indicated the following: 1) The error rate of the decision tree was lower;2) prediction success was higher;3) class separability was greater;and 4) overall accuracy was higher (high 90% range) at coarser segmentation scales. The inclusion of texture measures increased classification accuracies at nearly all segmentation scales, and entropy was the texture measure with the highest score in most decision trees. The results demonstrate that UAVs are viable platforms for rangeland monitoring and that the drawbacks of low-cost off-the-shelf digital cameras can be overcome by including texture measures and using object-based image analysis which is highly suitable for very high resolution imagery.

关键词： object-based classification rangelands scale texture unmanned aircraft

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Detection of urban environments using advanced land observing satellite phased array type L-band synthetic aperture radar data through different classification techniques

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JOURNAL OF APPLIED REMOTE SENSING 2016年第3期10卷

作者： Pradhan, Biswajeet Abdullahi, Saleh Seddighi, Younes Univ Putra Malaysia Fac Engn Geospatial Informat Sci Res Ctr Dept Civil Engn Serdang 43400 Selangor Malaysia

Urban environments are very dynamic phenomena, and it is essential to update urbanrelated information for various applications. In this regard, remotely sensed data have been utilized widely to extract and monitor urban land use and land cover changes. Particularly, synthetic aperture radar (SAR) data, due to several advantages of this technology in comparison to passive sensors, provides better performance especially in tropical regions. However, the methodological approaches for extraction of information from SAR images are another important task that needs to be considered appropriately. This paper attempts to investigate and compare the performance of different image classification techniques for extracting urban areas using advanced land observing satellite phased array type L-band synthetic aperture radar imagery. Several object-[such as rule based (RB), support vector machine (SVM) and K-nearest neighbor (K-NN)] and pixel-based [decision tree (DT)] classification techniques were implemented, and their results were compared in detail. The overall results indicated RB classification performed better than other techniques. Furthermore, DT method, due to its predefined rules, distinguished the land cover classes better than SVM and K-NN, which were based on training datasets. Nevertheless, this study confirms the potential of SAR data and object-based classification techniques in urban detection and land cover mapping. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： urban detection synthetic aperture radar object-based classification remote sensing advanced land observing satellite phased array type L-band synthetic aperture radar

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Tracking changes in soil organic carbon across the heterogeneous agricultural landscape of the Lower Fraser Valley of British Columbia

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 732卷 138994-138994页

作者： Paul, S. S. Dowell, L. Coops, N. C. Johnson, M. S. Krzic, M. Geesing, D. Smukler, S. M. Univ British Columbia Fac Land & Food Syst Soil Sci Program 2357 Main Mall Vancouver BC V6T 124 Canada Univ British Columbia Dept Forest Resources Management 2424 Main Mall Vancouver BC V6T 1Z4 Canada Univ British Columbia Inst Resources Environm & Sustainabil 2202 Main Mall Vancouver BC V6T 1Z4 Canada Univ British Columbia Dept Forest & Conservat Sci 2357 Main Mall Vancouver BC V6T 1Z4 Canada Govt British Columbia Minist Agr 1767 Angus Campbell Rd Abbotsford BC V3G 2M3 Canada

Increasing soil organic carbon (SOC) can improve the capacity of agricultural systems to both adapt to and mitigate climate change. Despite its importance, the current understanding of the magnitude or even the direction of SOC change in agricultural landscapes is limited. While changes in land use/land cover (LULC) and climate are among the main drivers of changes in SOC, their relative importance for the spatiotemporal assessment of SOC is unclear. This study evaluated LULC and SOC dynamics using archived and recent soil samples, remote sensing, and digital soil mapping in the Lower Fraser Valley of British Columbia, Canada. We combined both pixel- and object-based analysis of Landsat satellite imagery to assess LULC changes from 1984 to 2018. We achieved an overall accuracy of 81% and kappa coefficient of 0.77 for LULC classification using a random forest model. For predicting SOC for the same time period, we applied soil and vegetation indices derived from Landsat images, topographic indices, historic soil survey variables, and climate data in a random forest model. The SOC prediction of 2018 resulted in a coefficient of determination (R-2) of 0.67, concordance correlation coefficient (CCC) of 0.76, and normalized root mean square error (nRMSE) of 0.12. For 1984, the SOC prediction accuracies were 0.46, 0.58, and 0.18 for R-2, CCC, and nRMSE, respectively. We detected SOC loss in 61%, gain in 12%, while 27% remained unchanged across the study area. Although we detected large losses of SOC due to LULC change, the majority of the SOC losses across the landscape were attributed to areas that were remained in the same type of agricultural production since 1984. Climate variability did not, however, have a strong effect on SOC changes. These results can inform decision making in the study area to support sustainable LULC management for enhancing SOC sequestration.

关键词： Digital soil mapping Landsat object-based classification Random forest Remote sensing

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object-based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

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REMOTE SENSING 2016年第6期8卷 513-513页

作者： Aguilar, Manuel A. Nemmaoui, Abderrahim Novelli, Antonio Aguilar, Fernando J. Garcia Lorca, Andres Univ Almeria Dept Engn Ctra Sacramento S-N La Canada De San Urbano 04120 Almeria Spain Politecn Bari Via Orabona 4 I-70125 Bari Italy Univ Almeria Dept Geog Ctra Sacramento S-N La Canada De San Urbano 04120 Almeria Spain

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively.

关键词： Landsat 8 WorldView-2 time series object-based classification greenhouse mapping decision tree Moment Distance Index

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A Region-based GeneSIS Segmentation Algorithm for the classification of Remotely Sensed Images

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REMOTE SENSING 2015年第3期7卷 2474-2508页

作者： Mylonas, Stelios K. Stavrakoudis, Dimitris G. Theocharis, John B. Mastorocostas, Paris A. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece Aristotle Univ Thessaloniki Sch Forestry & Nat Environm Lab Forest Management & Remote Sensing Thessaloniki 54124 Greece Technol Educ Inst Cent Macedonia Dept Comp Engn Serres 62124 Greece

This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms.

关键词： image segmentation object-based classification watershed transform genetic algorithms marker selection segmentation fusionimage segmentation object-based classification watershed transform genetic algorithms marker selection segmentation fusion

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Land Use classification from Lidar Data and Ortho-Images in a Rural Area

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PHOTOGRAMMETRIC RECORD 2012年第140期27卷 401-422页

作者： Bujan, Sandra Gonzalez-Ferreiro, Eduardo Reyes-Bueno, Fabian Barreiro-Fernandez, Laura Crecente, Rafael Miranda, David Univ Santiago de Compostela Santiago De Compostela Spain

Obtaining information on the distribution of rural landscape types is an active research topic within Spanish rural studies. This paper presents a new hierarchical object-based classification method for the automatic detection of various land use classes in a rural area, combining lidar data and aerial images. In view of the upcoming availability of low-density lidar data (0.5 pulses/m2) for most of the territory of Spain, this paper assesses the feasibility and accuracy of the proposed method for various lidar data densities. Such an assessment was conducted using two approaches: firstly, based on the final classification, which produced an overall accuracy over 96% and a kappa index above 0.95 for the combinations of the aerial image and lidar data-sets with four different densities;and secondly, based solely on the areas classified as buildings. In the second approach, the accuracy of the classification for building detection at pixel and object level was assessed. The object-oriented classification of buildings produced an index of correctness of over 99% and an index of completeness of about 95%. The results reveal a high agreement between classification and ground truth data.

关键词： assessment accuracy data fusion detection of buildings lidar data object-based classification ortho-images

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