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Accuracy Assessment Measures for object Extraction from Remote Sensing images

REMOTE SENSING 2018年第2期10卷 303-303页

作者： Cai, Liping Shi, Wenzhong Miao, Zelang Hao, Ming Qufu Normal Univ Sch Geog & Tourism Rizhao 276826 Peoples R China Minist Land & Resource Key Lab Coastal Zone Exploitat & Protect Nanjing 210024 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Hong Kong Peoples R China Cent S Univ Sch Geosci & Infophys Changsha 410012 Hunan Peoples R China Cent S Univ Key Lab Metallogen Predict Nonferrous Met & Geol Changsha 410012 Hunan Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Peoples R China

object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm.

关键词： object-based image analysis accuracy assessment feature similarity distance difference

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object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2018年第7期190卷 376-376页

作者： Aslami, Farnoosh Ghorbani, Ardavan Univ Mohaghegh Ardabili Fac Humanities Dept Nat Geog Ardebil Iran Univ Mohaghegh Ardabili Fac Agr Technol & Nat Resources Dept Nat Resources Ardebil *** Iran

In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in the Ardabil province in the northwest of Iran, was detected using an object-based method. Landsat images including Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Operational Land imager (OLI) were used. Preprocessing methods, including geometric and radiometric correction, and topographic normalization were performed. image processing was conducted according to object-based image analysis using the nearest neighbor algorithm. An accuracy assessment was conducted using overall accuracy and Kappa statistics. Results show that maps obtained from images for 1987, 2002, and 2013 had an overall accuracy of 91.76, 91.06, and 93.00%, and a Kappa coefficient of 0.90, 0.83, and 0.91, respectively. Change detection between 1987 and 2013 shows that most of the rangelands (97,156.6 ha) have been converted to dry farming;moreover, residential and other urban land uses have also increased. The largest change in land use has occurred for irrigated farming, rangelands, and dry farming, of which approximately 3539.8, 3086.9, and 2271.9 ha, respectively, have given way to urban land use for each of the studied years.

关键词： Remote sensing Land use/land cover Change detection object-based image analysis Landsat Ardabil province

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Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery

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REMOTE SENSING OF ENVIRONMENT 2017年 196卷 193-204页

作者： Gudex-Cross, David Pontius, Jennifer Adams, Alison Univ Vermont Rubenstein Sch Environm & Nat Resources Aiken Ctr 81 Carrigan Dr Burlington VT 05405 USA

Spatially-explicit tree species distribution maps are increasingly valuable to forest managers and researchers in light of the effects of climate change and invasive pests on forest resources. Traditional forest classifications are limited to broad classes of forest types with variable accuracy. Advanced remote sensing techniques, such as spectral unmixing and object-based image analysis, offer novel forest mapping approaches by quantifying proportional species composition at the pixel level and utilizing ancillary environmental data for forest classifications. This is particularly useful in the Northeastern region of the United States where species composition is often mixed. Here we employed a hierarchical forest mapping approach using spectral unmixing of multi-temporal Landsat imagery to quantify percent basal area for ten common tree species/genera across northern New York and Vermont. Basal area maps were then refined using an object-based ruleset to produce a thematic forest classification. Validation with 50 field inventory plots covering a range of species compositions indicated that the quality of percent basal area mapping largely reflected the number of "pure" (>80% BA) endmember plots available for calibration, with more common species mapped at a higher accuracy (i.e. Acer saccharum, adj. r(2) = 0.44, compared to Populus sp., adj. r(2) = 0.24). The resulting thematic forest classification mapped 15 forest classes (nine species/genus level and six common species assemblages) with overall accuracy = 42%, KHAT = 33%, fuzzy accuracy = 86% at the pixel level, and 38%, KHAT = 29%, fuzzy accuracy = 84% at the object level. Using the validation plots to compare existing forest classification products, this hierarchical approach provided more class detail (11 represented classes) and higher accuracy than the National Forest Type Map (six represented classes, overall accuracy 18%, fuzzy accuracy 70%), LANDFIRE (five represented classes, overall accuracy 28%, fuz

关键词： Remote sensing Basal area mapping Biophysical modeling Hierarchical classification object-based image analysis

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Nearshore Benthic Habitat Mapping based on Multi-Frequency, Multibeam Echosounder Data Using a Combined object-based Approach: A Case Study from the Rowy Site in the Southern Baltic Sea

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REMOTE SENSING 2018年第12期10卷 1983-1983页

作者： Janowski, Lukasz Trzcinska, Karolina Tegowski, Jaroslaw Kruss, Aleksandra Rucinska-Zjadacz, Maria Pocwiardowski, Pawel Univ Gdansk Inst Oceanog Al Marszalka Pilsudskiego 46 PL-81378 Gdynia Poland NORBIT Poland Sp Zoo Al Niepodleglosci 813-815-24 PL-81810 Sopot Poland

Recently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. based on ground-truth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.

关键词： habitat mapping multibeam echosounder multi-frequency image processing feature selection object-based image analysis

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Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite images

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REMOTE SENSING 2018年第2期10卷 281-281页

作者： Zhang, Xiuyuan Du, Shihong Wang, Qiao Zhou, Weiqi Peking Univ Inst Remote Sensing & GIS Beijing 100871 Peoples R China Minist Environm Protect Satellite Environm Ctr Beijing 100094 Peoples R China Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China

Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese citiesBeijing, Putian, and Zhuhaiand generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery;different categories of functional zones extracted by using different scale parameters;and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

关键词： urban landscape functional zone object-based image analysis multiscale image segmentation VHR images

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Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年 130卷 13-31页

作者： Mandianpari, Masoud Salehi, Bahram Mohammadimanesh, Fariba Motagh, Mandi Mem Univ Newfoundland C CORE St John NF A1B 3X5 Canada Mem Univ Newfoundland Dept Elect Engn St John NF A1B 3X5 Canada GFZ German Res Ctr Geosci Dept Geodesy Sect Remote Sensing D-14473 Potsdam Germany Leibniz Univ Hannover Inst Photogrammetry & GeoInformat D-30167 Hannover Germany

Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant species. In this study, a novel hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a sub-region located in the north eastern portion of the Avalon Peninsula. Particularly, multi polarization and multi -frequency SAR data, including X-band TerraSAR-X single polarized (HH), L-band ALOS-2 dual polarized (HH/HV), and C-band RADARSAT-2 fully polarized images, were applied in different classification levels. First, a SAR backscatter analysis of different land cover types was performed by training data and used in Level-I classification to separate water from non-water classes. This was followed by Level-II classification, wherein the water class was further divided into shallow-and deep water classes, and the non-water class was partitioned into herbaceous and non-herbaceous classes. In Level-III classification, the herbaceous class was further divided into bog, fen, and marsh classes, while the non-herbaceous class was subsequently partitioned into urban, upland, and swamp classes. In Level-II and -III classifications, different polarimetric decomposition approaches, including Cloude-Pottier, Freeman-Durden, Yamaguchi decompositions, and Kennaugh matrix elements were extracted to aid the RF classifier. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. The importance of input features was also determined using the variable importance obtained by RF. It was found that the Kennaugh matrix elements, Yamaguchi, and Freeman-Durden decompositions were the most important parameters f

关键词： Wetland classification Polarimetric Synthetic Aperture Radar Random Forest object-based image analysis Kennaugh matrix

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Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery

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COMPUTERS & GEOSCIENCES 2017年 103卷 1-11页

作者： Canovas-Garcia, Fulgencio Alonso-Sarria, Francisco Gomariz-Castillo, Francisco Onate-Valdivieso, Fernando Univ Tecn Particular Loja Dept Geol & Minas & Ingn Civil San Cayetano Alto S-N Loja Ecuador Univ Murcia Inst Univ Agua & Medio Ambiente Edificio DCampus Espinardo S-N E-30100 Murcia Spain Univ Murcia Inst Euromediterrdneo Agua Edificio D S-N E-30100 Murcia Spain Univ Cuenca Dept Ingn Civil Av 12 Abril & Av Loja S-N Cuenca Ecuador

Random forest is a classification technique widely used in remote sensing. One of its advantages is that it produces an estimation of classification accuracy based on the so called out-of-bag cross-validation method. It is usually assumed that such estimation is not biased and may be used instead of validation based on an external data-set or a cross-validation external to the algorithm. In this paper we show that this is not necessarily the case when classifying remote sensing imagery using training areas with several pixels or objects. According to our results, out-of-bag cross-validation clearly overestimates accuracy, both overall and per class. The reason is that, in a training patch, pixels or objects are not independent (from a statistical point of view) of each other;however, they are split by bootstrapping into in bag and out-of-bag as if they were really independent. We believe that putting whole patch, rather than pixels/objects, in one or the other set would produce a less biased out-of-bag cross-validation. To deal with the problem, we propose a modification of the random forest algorithm to split training patches instead of the pixels (or objects) that compose them. This modified algorithm does not overestimate accuracy and has no lower predictive capability than the original. When its results are validated with an external data-set, the accuracy is not different from that obtained with the original algorithm. We analysed three remote sensing images with different classification approaches (pixel and object based);in the three cases reported, the modification we propose produces a less biased accuracy estimation.

关键词： Classification Random forest object-based image analysis Bagging Statistical independence

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Local and global evaluation for remote sensing image segmentation

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年第Aug.期130卷 256-276页

作者： Su, Tengfei Zhang, Shengwei Inner Mongolia Agr Univ Coll Water Conservancy & Civil Engn Hohhot 010018 Peoples R China

In object-based image analysis, how to produce accurate segmentation is usually a very important issue that needs to be solved before image classification or target recognition. The study for segmentation evaluation method is key to solving this issue. Almost all of the existent evaluation strategies only focus on the global performance assessment. However, these methods are ineffective for the situation that two segmentation results with very similar overall performance have very different local error distributions. To overcome this problem, this paper presents an approach that can both locally and globally quantify segmentation incorrectness. In doing so, region-overlapping metrics are utilized to quantify each reference geo-object's over and under-segmentation error. These quantified error values are used to produce segmentation error maps which have effective illustrative power to delineate local segmentation error patterns. The error values for all of the reference geo-objects are aggregated through using area weighted summation, so that global indicators can be derived. An experiment using two scenes of very different high resolution images showed that the global evaluation part of the proposed approach was almost as effective as other two global evaluation methods, and the local part was a useful complement to comparing different segmentation results. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： object-based image analysis Remote sensing image segmentation Local evaluation Region over-lapping

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Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 143卷 49-56页

作者： Wachholz de Souza, Carlos Henrique Camargo Lamparelli, Rubens Augusto Rocha, Jansle Vieira Graziano Magalhaes, Paulo Sergio Univ Estadual Campinas Sch Agr Engn BR-13083875 Campinas SP Brazil Univ Estadual Campinas Interdisciplinary Ctr Energy Planning BR-13083896 Campinas SP Brazil

The use of unmanned aerial vehicles (UAVs) as remote sensing platforms has tremendous potential for describing detailed site-specific features of crops, especially in early post-emergence, which was not possible previously with satellite images. This article describes an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. The procedure consists of three consecutive phases: (1) identification of sugarcane planting rows, (2) identification of the existent sugarcane within the crop rows, and (3) skip extraction and creation of field-extent crop maps. Results based on experimental fields achieved skip rates of between 2.29% and 10.66%, indicating a planting operation with excellent and good quality, respectively. The relationship of estimated versus observed skip length had a coefficient of determination of 0.97, which was confirmed by the value of the enhanced Wilmott concordance coefficient of 0.92, indicating good agreement. The OBIA procedure allowed a high level of automation and adaptability, and it provided useful information for decision making, agricultural monitoring, and the reduction of operational costs.

关键词： object-based image analysis Planting rows GIS Skip rate Decision making

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Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷

作者： Beaumont, Benjamin Grippa, Tais Lennert, Moritz Vanhuysse, Sabine Stephenne, Nathalie Wolff, Eleonore Univ Libre Bruxelles Inst Gest Environm & Amanagement Terr CP130-03 Brussels Belgium Inst Sci Serv Publ Remote Sensing & Geodata Unit Liege Belgium

Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i. e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15 m(2). Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed. c The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

关键词： urban land cover object-based image analysis supervised classification submeter remote sensing optical imagery light detection and ranging

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