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Optimal Combination of Classification Algorithms and Feature Ranking Methods for object-based Classification of Submeter Resolution Z/I-Imaging DMC imagery

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REMOTE SENSING 2015年第4期7卷 4651-4677页

作者： Canovas-Garcia, Fulgencio Alonso-Sarria, Francisco Univ Murcia Inst Univ Agua & Medio Ambiente E-30100 Murcia Spain Univ Tecn Particular Loja Dept Geol & Minas & Ingn Civil Loja Ecuador

object-based image analysis allows several different features to be calculated for the resulting objects. However, a large number of features means longer computing times and might even result in a loss of classification accuracy. In this study, we use four feature ranking methods (maximum correlation, average correlation, Jeffries-Matusita distance and mean decrease in the Gini index) and five classification algorithms (linear discriminant analysis, naive Bayes, weighted k-nearest neighbors, support vector machines and random forest). The objective is to discover the optimal algorithm and feature subset to maximize accuracy when classifying a set of 1,076,937 objects, produced by the prior segmentation of a 0.45-m resolution multispectral image, with 356 features calculated on each object. The study area is both large (9070 ha) and diverse, which increases the possibility to generalize the results. The mean decrease in the Gini index was found to be the feature ranking method that provided highest accuracy for all of the classification algorithms. In addition, support vector machines and random forest obtained the highest accuracy in the classification, both using their default parameters. This is a useful result that could be taken into account in the processing of high-resolution images in large and diverse areas to obtain a land cover classification.

关键词： random forest feature selection object-based image analysis Hughes effect photogrammetric camera classification

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Potential of multitemporal Gaofen-1 panchromatic/multispectral images for crop classification: case study in Xinjiang Uygur Autonomous Region, China

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JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷 096035-1-096035-15页

作者： Hao, Pengyu Wang, Li Niu, Zheng Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Gaofen-1 panchromatic/multispectral (GF-1 PMS) data have both high spatial and temporal resolutions, and this research aims at evaluating the potential of GF-1 PMS data for crop classification. Three PMS images (at days 110, 192, and 274) were acquired in Manas County of Xinjiang. The images were first segmented and all objects were then visually interpreted based on ground reference data. Some indices and textual features were then extracted at the object level. Subsequently, the Jeffries-Matusita (JM) distance was employed to estimate the class separability among all pair-wise comparisons of each time period. Afterward, a random forest algorithm was used to calculate importance scores of all features and classify crop types for every possible image combination. Additionally, to evaluate the influence of feature number on classification accuracy, features were added one by one based on the importance of scores. The result showed that GF-1 PMS images with high-spatial resolution had the potential to identify the boundary of the crop fields. Relatively high JM distance (above 1.5) and classification accuracy (above 90%) indicated that day 192 image contributed the most to the crop identification in the study area. For multi-image combinations, days 110 to 192 combination can achieve high overall accuracy (around 93%) and more images cannot substantially improve the classification performance. As for features, normalized difference vegetation index and near infrared (NIR) band had the highest importance scores and textual features contributed to distinguishing tree from crop land. Finally, classification accuracy increased together with the augmentation of feature number when only a few features were used. After accuracies reached saturation points, however, more features only slightly improved the classification performance. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： random forest object-based image analysis feature importance Jeffries-Matusita distance classification accuracy cotton maize wheat

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Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2015年 42卷 106-114页

作者： Juel, Anders Groom, Geoffrey Brian Svenning, Jens-Christian Ejrnaes, Rasmus Aarhus Univ Dept Biosci Sect Biodivers & Conservat DK-8410 Kalo Ronde Denmark Aarhus Univ Dept Biosci Sect Ecoinformat & Biodivers DK-8000 Aarhus C Denmark

High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated. We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites. Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively. Evaluating classification models with different degrees of

关键词： Habitat structure object-based image analysis Machine learning Aerial orthophoto imagery Model transferability

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Determination of the age of oil palm from crown projection area detected from World View-2 multispectral remote sensing data: The case of Ejisu-Juaben district, Ghana

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2015年第Feb.期100卷 118-127页

作者： Chemura, Abel van Duren, Iris van Leeuwen, Louise M. Chinhoyi Univ Technol Dept Environm Sci & Technol Chinhoyi Zimbabwe Univ Twente Nat Resource Dept ITC NL-7500 AE Enschede Netherlands

Information about age of oil palm is important in sustainability assessments, carbon mapping, yield projections and precision agriculture. The aim of this study was to develop and test an approach to determine the age of oil palm plantations (years after planting) by combining high resolution multispectral remote sensing data and regression techniques using a case study of Ejisu-Juaben district of Ghana. Firstly, we determined the relationship between age and crown projection area of oil palms from sample fields. Secondly, we did hierarchical classification using object based image analysis techniques on WorldView-2 multispectral data to determine the crown projection areas of oil palms from remote sensing data. Finally, the crown projection areas obtained from the hierarchical classification were combined with the field-developed regression model to determine the age of oil palms at field level for a wider area. Field collected data showed a strong linear relationship between age and crown area of oil palm up to 13 years beyond which no relationship was observed. A user's accuracy of 80.6% and a producer's accuracy of 68.4% were obtained for the delineation of oil palm crowns while for delineation of non-crown objects a user's accuracy of 65.6% and a producer's accuracy of 78.6% were obtained, with an overall accuracy of 72.8% for the OBIA delineation. Automatic crown projection area delineation from remote sensing data produced crown projection areas which closely matched the field measured crown areas except for older oil palms (13+ years) where the error was greatest. Combining the remote sensing detected crown projection area and the regression model accurately estimated oil palm ages for 27.9% of the fields and had an estimation error of 1 year or less for 74.6% of the fields and an error of a maximum 2 years for 92.4% of the fields. The results showed that 6 and 11 year old oil palm stands were dominating age categories in the study area. Although the method

关键词： Age detection Oil palm World View-2 object-based image analysis

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A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery

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GEOCARTO INTERNATIONAL 2015年第8期30卷 937-961页

作者： Canovas-Garcia, F. Alonso-Sarria, F. Univ Cuenca Dept Ingn Civil Cuenca Ecuador Univ Murcia Inst Univ Agua & Medio Ambiente Murcia Spain

The results obtained using the object-based image analysis approach for remote sensing image analysis depend strongly on the quality of the segmentation step. In this paper, to optimize the scale parameter in a multiresolution segmentation, we analyse a high-resolution image of a large and heterogeneous agricultural area. This approach is based on using a set of agricultural plots extracted from official maps as uniform spatial units. The scale parameter is then optimized in each uniform spatial unit. Intra-object and inter-object heterogeneity measurements are used to evaluate each segmentation. To avoid subsegmentation, some oversegmentation is allowed, but is attenuated in a second step using the spectral difference segmentation algorithm. The statistical distribution of the scale parameter is not equal in all land uses, indicating the soundness of this local approach. A quantitative assessment of the results was also conducted for the different land covers. The results indicate that the spectral contrast between objects is larger with the local approach than with the global approach. These differences were statistically significant in all land uses except irrigated fruit trees and greenhouses. In the absence of subsegmentation, this suggests that the objects will be placed far apart in the space of variables, even if they are very close in the physical space. This is an obvious advantage in a subsequent classification of the objects.

关键词： object-based image analysis segmentation optimization eCognition(TM)

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Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach

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NATURAL HAZARDS 2015年第2期78卷 1021-1044页

作者： Dou, Jie Li, Xia Yunus, Ali P. Paudel, Uttam Chang, Kuan-Tsung Zhu, Zhongfan Pourghasemi, Hamid Reza Univ Tokyo Dept Nat Environm Studies Kashiwa Chiba 2778568 Japan Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Guangdong Peoples R China Minghsin Univ Sci & Technol Dept Civil Engn & Environm Informat Hsinchu Taiwan Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China Shiraz Univ Coll Agr Dept Nat Resources & Environm Shiraz Iran

Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes. The proposed method will be useful for preparing hazard maps that express the relative probability of a collapse in similar regions.

关键词： Sinkholes object-based image analysis image segmentation Hazard mapping

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Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite imagery

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REMOTE SENSING 2015年第7期7卷 8271-8299页

作者： Nikfar, Maryam Zoej, Mohammad Javad Valadan Mokhtarzade, Mehdi Shoorehdeli, Mahdi Aliyari KN Toosi Univ Technol Fac Geodesy & Geomat Engn Dept Photogrammetry & Remote Sensing Tehran *** Iran KN Toosi Univ Technol Fac Elect Engn Control Dept Tehran *** Iran

The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2.

关键词： type-2 fuzzy sets genetic cooperative-competitive learning object-based image analysis road detection satellite imagery

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Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

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Information Processing in Agriculture 2015年第1期2卷 25-36页

作者： Chinsu Lin Chao-Cheng Wu Khongor Tsogt Yen-Chieh Ouyang Chein-I Chang Department of Forestry and Natural Resources National Chiayi University300 University Rd.Chiayi 60004Taiwan Department of Electrical Engineering National Taipei University of Technology1 Zhongxiao E.Rd.Sec.3Taipei 10608Taiwan Department of Electrical Engineering National Chung Hsing University250Kuo Kuang Rd.Taichung 402Taiwan Department of Computer Science and Electrical Engineering UMBC1000 Hilltop CircleBaltimoreMD 20250USA

Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the *** LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity *** paper examined effects of pansharpening and atmospheric correction on LULC ***-based Support Vector Machine(OB-SVM)and Pixel-based Maximum Likelihood Classifier(PB-MLC)were applied for LULC *** showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral ***,pansharpening plays much more important roles on the classification accuracy than the atmospheric *** can help to increase classification accuracy by 12%on average compared to the ones without ***-MLC and OB-SVM achieved similar classification *** study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82%and 89%respectively.A combination of atmospheric correction,pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.

关键词： LULC Remote sensing object-based image analysis Pixel-based image analysis Maximum likelihood classifier(MLC) Support vector machine(SVM)

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High-resolution maps of forest-urban watersheds present an opportunity for ecologists and managers

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LANDSCAPE ECOLOGY 2015年第2期30卷 313-323页

作者： Stueve, Kirk M. Hollenhorst, Tom P. Kelly, John R. Johnson, Lucinda B. Host, George E. Univ Minnesota Nat Resources Res Inst Duluth MN 55811 USA US EPA Midcontinent Ecol Div Duluth MN 55804 USA

Green infrastructure may improve water quality and mitigate flooding in forest-urban watersheds, but reliably quantifying all benefits is challenging because most land cover maps depend on moderate- to low-resolution data. Complex and spatially heterogeneous landscapes that typify forest-urban watersheds are not fully represented with these types of data. Hence important questions concerning how green infrastructure influences water quality and quantity at different spatial scales remain unanswered. Demonstrate the feasibility of creating novel high-resolution land cover maps across entire watersheds and highlight deficiencies of standard land cover products. We used object-based image analysis (OBIA) to create high-resolution (0.5 m) land cover maps and detect tree canopy overlapping impervious surfaces for a representative forest-urban watershed in Duluth, MN, USA. Unbiased estimates of accuracy and area were calculated and compared with similar metrics for the 30-m National Land Cover Database (NLCD). Mapping accuracies for the high-resolution land cover and canopy overlap maps were similar to 90 %. Error-adjusted estimates of area indicated that impervious surfaces comprised similar to 21 % of the watershed, tree canopy overlapped similar to 10 % of impervious surfaces, and that three high-resolution land cover classes differed significantly from similar NLCD classes. OBIA can efficiently generate high-resolution land cover products of entire watersheds that are appropriate for research and inclusion in the decision-making process of managers. Metrics derived from these products will likely differ from standard land cover maps and may produce new insights, especially when considering the unprecedented opportunity to evaluate fine-scale spatial heterogeneity across watersheds.

关键词： Aerial photography LiDAR Green infrastructure Impervious surfaces object-based image analysis Scaling

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Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes

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GEOCARTO INTERNATIONAL 2015年第10期30卷 1113-1129页

作者： Estoque, Ronald C. Murayama, Yuji Akiyama, Chiaki Mizutani Univ Tsukuba Fac Life & Environm Sci Tsukuba Ibaraki Japan Natl Inst Environm Studies Tsukuba Ibaraki Japan

With the increasing availability of high-spatial-resolution remote sensing imageries and with the observed limitations of pixel-based techniques, the development and testing of geographic object-based image analysis (GEOBIA) techniques for image classification have become one of the main research areas in geospatial science. This paper examines and compares the classification performance of a pixel-based method and an object-based method as applied to high- (QuickBird satellite image) and medium- (Landsat TM image) spatial-resolution imageries in the context of urban and suburban landscapes. For the pixel-based classification, the maximum-likelihood supervised classification approach was employed. And for the object-based classification, the pixel-based classified maps were integrated with a set of image segments produced using various calibrations. The results show evidence that the object-based method can produce classifications that are more accurate for both high- and medium-spatial- resolution imageries in the context of urban and suburban landscapes.

关键词： object-based image analysis GEOBIA land use land cover pixel-based image analysis

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