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Identification and classification of structural soil conservation measures based on very high resolution stereo satellite data

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2017年 193卷 592-606页

作者： Eckert, Sandra Ghebremicael, Selamawit Tesfay Hurni, Hans Kohler, Thomas Univ Bern Ctr Dev & Environm Hallerstr 10 CH-3012 Bern Switzerland Univ Bern Inst Geog Dept Integrated Geog Hallerstr 12 CH-3012 Bern Switzerland POB 9566 Asmera Eritrea

Land degradation affects large areas of land around the globe, with grave consequences for those living off the land. Major efforts are being made to implement soil and water conservation measures that counteract soil erosion and help secure vital ecosystem services. However, where and to what extent such measures have been implemented is often not well documented. Knowledge about this could help to identify areas where soil and water conservation measures are successfully supporting sustainable land management, as well as areas requiring urgent rehabilitation of conservation structures such as terraces and bunds. This study explores the potential of the latest satellite-based remote sensing technology for use in assessing and monitoring the extent of existing soil and water conservation structures. We used a set of very high resolution stereo Geoeye-1 satellite data, from which we derived a detailed digital surface model as well as a set of other spectral, terrain, texture, and filtered information layers. We developed and applied an object-based classification approach, working on two segmentation levels. On the coarser level, the aim was to delimit certain landscape zones. Information about these landscape zones is useful in distinguishing different types of soil and water conservation structures, as each zone contains certain specific types of structures. On the finer level, the goal was to extract and identify different types of linear soil and water conservation structures. The classification rules were based mainly on spectral, textural, shape, and topographic properties, and included object relationships. This approach enabled us to identify and separate from other classes the majority (78.5%) of terraces and bunds, as well as most hillside terraces (81.25%). Omission and commission errors are similar to those obtained by the few existing studies focusing on the same research objective but using different types of remotely sensed data. based on our results,

关键词： Very high resolution images object-based image analysis Digital surface model Soil conservation Agricultural terraces Drylands

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Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 134卷 151-159页

作者： Gilbertson, Jason Kane Kemp, Jaco van Niekerk, Adriaan Univ Stellenbosch Dept Geog & Environm Studies Stellenbosch South Africa Univ Western Australia Sch Plant Biol 35 Stirling Hwy Crawley WA 6009 Australia

This study evaluates the potential of pan-sharpening multi-temporal Landsat 8 imagery for the differentiation of crops in a Mediterranean climate. Five Landsat 8 images covering the phenological stages of seven major crops types in the study area (Cape Winelands, South Africa) were acquired. A statistical pan-sharpening algorithm was used to increase the spatial resolution of the 30 m multispectral bands to 15 m. The pan-sharpened images and original multispectral bands were used to generate two sets of input features at 30 and 15 m resolutions respectively. The two sets of spatial variables were separately used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forests (RF) machine learning classifiers. The analyses were carried out in both the object based image analysis (OBIA) and pixel-based image analysis (PBIA) paradigms. For the OBIA experiments, three image segmentation scenarios were tested (good, over and under segmentation). The PBIA experiments were carried out at 30 m and 15 m resolutions. The results show that pan-sharpening led to dramatic (,-15%) improvements in classification accuracies in both the PBIA and OBIA approaches. Compared to the other classifiers, SVM consistently produced superior results. When applied to the pah-sharpened imagery SVM produced an overall accuracy of nearly 96% using OBIA, while PBIA's overall accuracy was 1.63% lower. We conclude that pan-sharpening Landsat 8 imagery is highly beneficial for classifying agricultural fields whether an object- or pixel-based approach is used. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Pan-sharpening Machine learning object-based image analysis Supervised classification Crop type mapping

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Detection of potato beetle damage using remote sensing from small unmanned aircraft systems

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JOURNAL OF APPLIED REMOTE SENSING 2017年第2期11卷 026013-1-026013-10页

作者： Hunt, E. Raymond, Jr. Rondon, Silvia I. USDA ARS Beltsville Agr Res Ctr Hydrol & Remote Sensing Lab Beltsville MD 20705 USA Oregon State Univ Hermiston Agr Res & Extens Ctr Hermiston OR USA

Colorado potato beetle (CPB) adults and larvae devour leaves of potato and other solanaceous crops and weeds, and may quickly develop resistance to pesticides. With early detection of CPB damage, more options are available for precision integrated pest management, which reduces the amount of pesticides applied in a field. Remote sensing with small unmanned aircraft systems (sUAS) has potential for CPB detection because low flight altitudes allow image acquisition at very high spatial resolution. A five-band multispectral sensor and up-looking incident light sensor were mounted on a six-rotor sUAS, which was flown at altitudes of 60 and 30 m in June 2014. Plants went from visibly undamaged to having some damage in just 1 day. Whole-plot normalized difference vegetation index (NDVI) and the number of pixels classified as damaged (0.70 <= NDVI <= 0.80) were not correlated with visible CPB damage ranked from least to most. Area of CPB damage estimated using object-based image analysis was highly correlated to the visual ranking of damage. Furthermore, plant height calculated using structure- from-motion point clouds was related to CPB damage, but this method required extensive operator intervention for success. object-based image analysis has potential for early detection based on high spatial resolution sUAS remote sensing. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： Solanum tuberosum Colorado potato beetle Leptinotarsa decemlineata multirotor drone spectral vegetation indices object-based image analysis structure-from-motion point clouds

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Classification of semiurban landscapes from very high-resolution satellite images using a regionalized multiscale segmentation approach

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JOURNAL OF APPLIED REMOTE SENSING 2017年第3期11卷

作者： Kavzoglu, Taskin Erdemir, Merve Yildiz Tonbul, Hasan Gebze Tech Univ Dept Geomat Engn Gebze Turkey

In object-based image analysis, obtaining representative image objects is an important prerequisite for a successful image classification. The major threat is the issue of scale selection due to the complex spatial structure of landscapes portrayed as an image. This study proposes a two-stage approach to conduct regionalized multiscale segmentation. In the first stage, an initial high-level segmentation is applied through a "broadscale," and a set of image objects characterizing natural borders of the landscape features are extracted. Contiguous objects are then merged to create regions by considering their normalized difference vegetation index resemblance. In the second stage, optimal scale values are estimated for the extracted regions, and multiresolution segmentation is applied with these settings. Two satellite images with different spatial and spectral resolutions were utilized to test the effectiveness of the proposed approach and its transferability to different geographical sites. Results were compared to those of image-based single-scale segmentation and it was found that the proposed approach outperformed the single-scale segmentations. Using the proposed methodology, significant improvement in terms of segmentation quality and classification accuracy (up to 5%) was achieved. In addition, the highest classification accuracies were produced using fine-scale values. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis object-based classification very high-resolution satellite images multiscale segmentation landscape dynamics

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Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年第Jan.期123卷 104-113页

作者： Wang, Min Cui, Qi Wang, Jie Ming, Dongping Lv, Guonian Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China

In this paper, we first propose several novel concepts for object-based image analysis, which include line based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Region-line primitive association framework Raft cultivation High resolution remote sensing Information extraction Multi-scale object-based image analysis

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Unsupervised change detection in VHR remote sensing imagery - an object-based clustering approach in a dynamic urban environment

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2017年 54卷 15-27页

作者： Leichtle, Tobias Geiss, Christian Wurm, Michael Lakes, Tobia Taubenboeck, Hannes Co Remote Sensing & Environm Res SLU Kohlsteiner Str 5 D-81243 Munich Germany German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Munchner Str 20 D-82234 Wessling Germany Humboldt Univ Dept Geog Rudower Chaussee 16 D-12489 Berlin Germany

Monitoring of changes is one of the most important inherent capabilities of remote sensing. The steadily increasing amount of available very-high resolution (VHR) remote sensing imagery requires highly automatic methods and thus, largely unsupervised concepts for change detection. In addition, new procedures that address this challenge should be capable of handling remote sensing data acquired by different sensors. Thereby, especially in rapidly changing complex urban environments, the high level of detail present in VHR data indicates the deployment of object-based concepts for change detection. This paper presents a novel object-based approach for unsupervised change detection with focus on individual buildings. First, a principal component analysis together with a unique procedure for determination of the number of relevant principal components is performed as a predecessor for change detection. Second, k-means clustering is applied for discrimination of changed and unchanged buildings. In this manner, several groups of object-based difference features that can be derived from multi-temporal VHR data are evaluated regarding their discriminative properties for change detection. In addition, the influence of deviating viewing geometries when using VHR data acquired by different sensors is quantified. Overall, the proposed workflow returned viable results in the order of kappa statistics of 0.8-0.9 and beyond for different groups of features, which demonstrates its suitability for unsupervised change detection in dynamic urban environments. With respect to imagery from different sensors, deviating viewing geometries were found to deteriorate the change detection result only slightly in the order of up to 0.04 according to kappa statistics, which underlines the robustness of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Change detection object-based image analysis Principal component analysis Clustering Very-high resolution (VHR) remote sensing Urban environment

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Technical design and system implementation of region-line primitive association framework

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年 130卷 202-216页

作者： Wang, Min Xing, Jinjin Wang, Jie Lv, Guonian Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

Apart from regions, image edge lines are an important information source, and they deserve more attention in object-based image analysis (OBIA) than they currently receive. In the region-line primitive association framework (RLPAF), we promote straight-edge lines as line primitives to achieve powerful OBIAs. Along with regions, straight lines become basic units for subsequent extraction and analysis of OBIA features. This study develops a new software system called remote-sensing knowledge finder (RSFinder) to implement RLPAF for engineering application purposes. This paper introduces the extended technical framework, a comprehensively designed feature set, key technology, and software implementation. To our knowledge, RSFinder is the world's first OBIA system based on two types of primitives, namely, regions and lines. It is fundamentally different from other well-known region-only-based OBIA systems, such as eCogntion and ENVI feature extraction module. This paper has important reference values for the development of similarly structured OBIA systems and line-involved extraction algorithms of remote sensing information. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Region-line primitive association framework object-based image analysis image segmentation Region Straight line High-resolution remote sensing Information extraction

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Vegetation classification of Coffea on Hawaii Island using WorldView-2 satellite imagery

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JOURNAL OF APPLIED REMOTE SENSING 2017年第4期11卷

作者： Gaertner, Julie Genovese, Vanessa Brooks Potter, Christopher Sewake, Kelvin Manoukis, Nicholas C. ARS Daniel K Inouye US Pacific Basin Agr Res Ctr USDA Hilo HI 96720 USA Univ Hawaii Manoa Coll Trop Agr & Human Resources Hilo HI USA Calif State Univ Monterey Bay Div Sci & Environm Policy Seaside CA USA NASA Ames Res Ctr Earth Syst Sci Moffett Field CA 94035 USA Univ Hawaii Manoa Coll Trop Agr & Human Resources Honolulu HI 96822 USA

Coffee is an important crop in tropical regions of the world;about 125 million people depend on coffee agriculture for their livelihoods. Understanding the spatial extent of coffee fields is useful for management and control of coffee pests such as Hypothenemus hampei and other pests that use coffee fruit as a host for immature stages such as the Mediterranean fruit fly, for economic planning, and for following changes in coffee agroecosystems over time. We present two methods for detecting Coffea arabica fields using remote sensing and geospatial technologies on WorldView-2 high-resolution spectral data of the Kona region of Hawaii Island. The first method, a pixel-based method using a maximum likelihood algorithm, attained 72% producer accuracy and 69% user accuracy (68% overall accuracy) based on analysis of 104 ground truth testing polygons. The second method, an object-based image analysis (OBIA) method, considered both spectral and textural information and improved accuracy, resulting in 76% producer accuracy and 94% user accuracy (81% overall accuracy) for the same testing areas. We conclude that the OBIA method is useful for detecting coffee fields grown in the open and use it to estimate the distribution of about 1050 hectares under coffee agriculture in the Kona region in 2012. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： coffee berry borer remote sensing object-based image analysis

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Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaiso, Chile

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NATURAL HAZARDS 2017年第1-Sup期86卷 81-105页

作者： Geiss, Christian Schauss, Anne Riedlinger, Torsten Dech, Stefan Zelaya, Cecilia Guzman, Nicolas Hube, Mathias A. Arsanjani, Jamal Jokar Taubenboeck, Hannes German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Munchner Str 20 D-82234 Oberpfaffenhofen Germany Chilean Navy Hydrog & Oceanog Serv SHOA Errazuriz Echaurren 254 Playa Ancha Valparaiso Chile Pontificia Univ Catolica Chile Vicuna Mackenna 4860 Santiago Chile Natl Res Ctr Integrated Nat Disaster Management C Vicuna Mackenna 4860 Santiago Chile Heidelberg Univ GISci Res Grp Berliner Str 48 D-69120 Heidelberg Germany

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatiotemporal variability of elements at risk, detailed information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very-high-resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed;however, the design of information extraction procedures with a high level of automatization remains challenging. In this paper, we uniquely combine remote sensing data and volunteered geographic information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in conjunction with the satellite imagery. This allows for learning a supervised algorithmic model (i.e., rotation forest) in order to extract relevant thematic classes of land use/land cover (LULC) from the satellite imagery. Extracted information is jointly deployed with information from the OSM data to estimate the number of buildings with regression techniques (i.e., a multi-linear model from ordinary least-square optimization and a nonlinear support vector regression model are considered). Analogously, urban LULC information is used in conjunction with OSM data to spatially disaggregate population information. Experimental results were obtained for the city of Valparaiso in Chile. Thereby,

关键词： Exposure Risk Vulnerability Remote sensing Volunteered geographic information Land-use-land-cover classification object-based image analysis Rotation forest Population disaggregation Tsunami

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An object-based SAR image iceberg detection algorithm applied to the Amundsen Sea

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REMOTE SENSING OF ENVIRONMENT 2017年 189卷 67-83页

作者： Mazur, A. K. Wahlin, A. K. Krezel, A. Univ Gothenburg Dept Marine Sci POB 460 S-40530 Gothenburg Sweden Univ Gdansk Inst Oceanog Al Marszalka Pilsudskiego 46 PL-81836 Gdynia Poland

An object-based method for automatic iceberg detection from Advanced Synthetic Aperture Radar (ASAR) images has been developed and applied in the Amundsen Sea, Antarctica. The automatic identification is based on brightness and spatial parameters of the ASAR images at five scale levels, and was verified with manual classification in four areas chosen to represent varying environmental conditions. The presented algorithm works comparatively well with images of the ocean in freezing temperatures and strong wind conditions, common in the Amundsen Sea. The detection rate was 96.2% which corresponds to 93.2% of the icebergs area, for all seasons. The algorithm generated 3.8% errors in the form of 'misses' and 7.0% of 'false alarms', mainly caused by the presence of ice floes. The method was applied on 432 radar images acquired in 2011 under different meteorological, oceanographic and sea ice conditions. As an output a map showing the probability of finding icebergs has been created. It shows that high probability coincides with depth contours and indicates a westward drift of the bergs throughout the whole region. (C) 2016 The Authors. Published by Elsevier Inc.

关键词： ENVISAT ASAR object-based image analysis Icebergs The Amundsen Sea

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