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36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Volpi, Michele Tuia, Devis Univ Zurich Dept Geog MultiModal Remote Sensing CH-8006 Zurich Switzerland

ISBN: (纸本)9781509033324

Land-cover and land-use semantic labeling in centimeter resolution imagery (ultra-high resolution) is mostly performed by supervised classification of informative descriptors extracted from spatially coherent but small objects (e.g. superpixels or patches). In this paper, we propose an extension of this reasoning by proposing a class-specific, multi-scale and bottom-up object proposal strategy to perform semantic labeling. Specifically, we rely on a fully trainable boundary (edge) detector, allowing us to extract class-specific objectproposals. Such proposals enable training rich appearance and object models as well as enhanced spatial reasoning. We evaluate the proposed strategy on the Vaihingen dataset with promising results.

关键词： object proposals object-based classification Semantic labeling Random forests Aerial imagery

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ESTIMATING WOOD VOLUME FOR PINUS BRUTIA TREES IN FOREST STANDS FROM QUICKBIRD-2 IMAGERY 23

ESTIMATING WOOD VOLUME FOR <i>PINUS BRUTIA</i> TREES IN FORE...

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

作者： Patias, Petros Stournara, Panagiota AUTH Dept Cadastre Photogrammetry & Cartog Lab Photogrammetry & Remote Sensing Thessaloniki Greece

Knowledge of forest parameters, such as wood volume, is required for a sustainable forest management. Collecting such information in the field is laborious and even not feasible in inaccessible areas. In this study, tree wood volume is estimated utilizing remote sensing techniques, which can facilitate the extraction of relevant information. The study area is the University Forest of Taxiarchis, which is located in central Chalkidiki, Northern Greece and covers an area of 58km(2). The tree species under study is the conifer evergreen species P. brutia (Calabrian pine). Three plot surfaces of 10m radius were used. VHR Quickbird-2 images are used in combination with an allometric relationship connecting the Tree Crown with the Diameter at breast height (Dbh), and a volume table developed for Greece. The overall methodology is based on individual tree crown delineation, based on (a) the marker-controlled watershed segmentation approach and (b) the GEographic object-based Image Analysis approach. The aim of the first approach is to extract separate segments each of them including a single tree and eventual lower vegetation, shadows, etc. The aim of the second approach is to detect and remove the "noisy" background. In the application of the first approach, the Blue, Green, Red, Infrared and PCA-1 bands are tested separately. In the application of the second approach, NDVI and image brightness thresholds are utilized. The achieved results are evaluated against field plot data. Their observed difference are between -5% to +10%.

关键词： Quickbird-2 Marker-controlled watershed segmentation object-based classification Tree wood volume Individual tree crown delineation

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URBAN LAND COVER/USE CHANGE DETECTION USING HIGH RESOLUTION SPOT 5 AND SPOT 6 IMAGES AND URBAN ATLAS NOMENCLATURE 23

URBAN LAND COVER/USE CHANGE DETECTION USING HIGH RESOLUTION ...

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

作者： Akay, S. S. Sertel, E. Istanbul Tech Univ Ctr Satellite Commun & Remote Sensing TR-34669 Istanbul Turkey Istanbul Tech Univ Dept Geomat Engn TR-34669 Istanbul Turkey

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. classification accuracy assessment was con

关键词： object-based classification Remote Sensing SPOT-5 SPOT-6 Urban Mapping Urban Change

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Flood mapping using VHR satellite imagery: a comparison between different classification approaches 7

Flood mapping using VHR satellite imagery: a comparison betw...

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SPIE Conference on Earth Resources and Environmental Remote Sensing/GIS Applications VII

作者： Franci, Francesca Boccardo, Piero Mandanici, Emanuele Roveri, Elena Bitelli, Gabriele Univ Bologna DICAM Viale Risorgimento 2 Bologna Italy Politecn Torino DIST Viale Matteoli 39 Turin Italy

ISBN: (数字)9781510604155

ISBN: (纸本)9781510604148;9781510604155

Various regions in Europe have suffered from severe flooding over the last decades. Flood disasters often have a broad extent and a high frequency. They are considered the most devastating natural hazards because of the tremendous fatalities, injuries, property damages, economic and social disruption that they cause. In this context, Earth Observation techniques have become a key tool for flood risk and damage assessment. In particular, remote sensing facilitates flood surveying, providing valuable information, e.g. flood occurrence, intensity and progress of flood inundation, spurs and embankments affected/threatened. The present work aims to investigate the use of Very High Resolution satellite imagery for mapping flood-affected areas. The case study is the November 2013 flood event which occurred in Sardinia region (Italy), affecting a total of 2,700 people and killing 18 persons. The investigated zone extends for 28 km(2) along the Posada river, from the Maccheronis dam to the mouth in the Tyrrhenian sea. A post-event SPOT6 image was processed by means of different classification methods, in order to produce the flood map of the analysed area. The unsupervised classification algorithm ISODATA was tested. A pixel-based supervised technique was applied using the Maximum Likelihood algorithm;moreover, the SPOT 6 image was processed by means of object-oriented approaches. The produced flood maps were compared among each other and with an independent data source, in order to evaluate the performance of each method, also in terms of time demand.

关键词： Flood mapping Sardinia object-based classification pixel-based classification disaster management SPOT 6 2013 flood event

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Identification of Agricultural Land Use in California Through Remote Sensing

Identification of Agricultural Land Use in California Throug...

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作者： Robinson, Todd Allen California Polytechnic State University

学位级别：M.Sc., Master of Science

Ground truthing actual crop types in an area can be expensive and timeconsuming. The California Department of Water Resources attempts to ground truth land use in each county in California every five years. However, this is limited by budgetary constraints and often results in infrequent (more than every ten years) surveying of many counties. An accurate accounting of crops growing in a region is important for a variety of purposes including farm production estimates, groundwater and surface water modeling, evapotranspiration estimation, water planning, research applications, etc. Agricultural land use is continually changing due to development and environmental factors. Currently, USDA NASS provides georeferenced land use maps of regions throughout the U.S. While these are beneficial, the accuracy is not very high for California due to the wide variety of crops grown throughout the state. California has an increasingly complex agricultural system which includes multi-crops changing on an annual and even semiannual basis, long growing seasons, and complex and flexible irrigation schedules. Remotely sensed data from available satellites are used to more accurately classify crop types within the Madera and Merced Counties of California’s Central Valley. An initial classification approach utilizing a simplified decision tree for a data subset of the area considered is presented. In order to accommodate the larger dataset at hand, a computer based approach is applied using the Nearest Neighbor classification algorithm in the computer program eCognition. Iterative analyses were performed to consider a range of scenarios with varying spectral inputs. The results show the methods presented can be beneficial in discriminating 24 of the major crop types from multi-temporal spectral data.

关键词： remote sensing land classification decision tree eCognition Phenology object-based classification

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object-based Multispectral Image Segmentation and classification 7

Object-based Multispectral Image Segmentation and Classifica...

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7th International Symposium on Telecommunications (IST)

作者： Mirzapour, Fardin Ghassemian, Hassan Tathiat Modares Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781479953592

In this paper an object-based method for multispectral image segmentation and classification is proposed. Normally, in remote sensing a scene is represented by pixel-based features. It is possible to reduce data redundancy by a segment-feature extraction process, where the segment-features, rather than the pixel-features, are used for multispectral scene representation and classification. object-based algorithms partition the observation space into a set of disjoint segments (called objects). Then, pixels belonging to each segment are represented by segment features. In this paper, an unsupervised segmentation algorithm based on statistical region merging (SRM) framework is presented. Also a partial differential equations (PDE) algorithm is suggested as a preprocessing phase to improve the segmentation results. Illustrative examples are presented, and the performance of the extracted object features for classification purposes is investigated. Results show significant classification performance improvement.

关键词： Multispectral Images Remote Sensing Segmentation object-based classification Image compression

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A FULLY ADAPTIVE object EXTRACTION TECHNIQUE USED FOR SPECTRAL-SPATIAL classification OF REMOTELY SENSED DATA

A FULLY ADAPTIVE OBJECT EXTRACTION TECHNIQUE USED FOR SPECTR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zehtabian, Amin Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781479979295

The effectiveness of object-based image classification approaches has been frequently addressed and discussed in literature, especially for remote sensing applications. Unlike the traditional pixel-wise methods, object-based classifiers benefit from a segmentation step before the classification process in order to generate objects. In this paper, we propose to use the Pixon concept for segmentation of the data. Meanwhile, in order to form objects which are spectrally homogenous, spatial smoothing is applied as a preprocessing step through using regularized nonlinear partial differential equations (RegAPDE). The parameters of RegAPDE as well as important thresholds used in the Pixon extraction technique are adaptively tuned using three different adaptation algorithms. We also propose to localize the smoothing process via separately applying the RegAPDE algorithm to individual partitions extracted from each layer of the hyperspectral datasets. To this end, a simple partitioning step based on Watershed transformation is used before the smoothing procedure.

关键词： object-based classification regularized partial differential equations adaptive parameters watershed

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Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

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WATER 2016年第12期8卷 584页

作者： Perea-Moreno, Alberto-Jesus Aguilera-Urena, Maria-Jesus Merono-De Larriva, Jose-Emilio Manzano-Agugliaro, Francisco Univ Cordoba Dept Appl Phys CEIA3 Campus Rabanales E-14071 Cordoba Spain Univ Cordoba Dept Graph Engn & Geomat CEIA3 Campus Rabanales E-14071 Cordoba Spain Univ Almeria CIAIMBITAL Res Ctr Agr & Food Biotechnol Almeria 04120 Spain Univ Almeria Dept Engn CEIA3 Almeria 04120 Spain

Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes;in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management.

关键词： water management golf course memory-prediction theory object-based classification unmanned aerial vehicle

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Interpretation of Forest Resources at the Individual Tree Level in Japanese Conifer Plantations Using Airborne LiDAR Data

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REMOTE SENSING 2016年第3期8卷 188-188页

作者： Deng, Songqiu Katoh, Masato Shinshu Univ Inst Mt Sci Minamiminowa Vill Nagano 3994598 Japan

More than 50% of the national lands in Japan have been surveyed by airborne laser scanning (ALS) data with different point densities;and developing an effective approach to take full advantage of these ALS data for forest management has thus become an urgent topic of study. This study attempted to assess the utility of ALS data for individual tree detection and species classification in a mixed forest with a high canopy density. For comparison, two types of tree tops and tree crowns in the study area were delineated by the individual tree crown (ITC) approach using the green band of the orthophoto imagery and the digital canopy height model (DCHM) derived from the ALS data, respectively. Then, the two types of tree crowns were classified into four classes-Pinus densiflora (Pd), Chamaecyparis obtusa (Co), Larix kaempferi (Lk), and broadleaved trees (Bl)-by a crown-based classification approach using different combinations of the three orthophoto bands with intensity and slope maps as follows: RGB (red, green and blue);RGB and intensity (RGBI);RGB and slope (RGBS);and RGB, intensity and slope (RGBIS). Finally, the tree tops were annotated with species attributes from the two best-classified tree crown maps, and the number of different tree species in each compartment was counted for comparison with the field data. The results of our study suggest that the combination of RGBIS yielded greater classification accuracy than the other combinations. In the tree crown classifications delineated by the green band and DCHM data, the improvements in the overall accuracy compared to the RGB ranged from 5.7% for the RGBS to 9.0% for the RGBIS and from 8.3% for the RGBS to 11.8% for the RGBIS. The laser intensity and slope derived from the ALS data may be valuable sources of information for tree species classification, and in terms of distinguishing species for the detection of individual trees, the findings of this study demonstrate the advantages of using DCHM instead of optical

关键词： object-based classification orthophoto airborne laser scanning digital canopy height model forest resource measurement individual tree crown approach 3D point clouds

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Hierarchical object-based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

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REMOTE SENSING 2016年第2期8卷 97-97页

作者： Demarchi, Luca Bizzi, Simone Piegay, Herve European Commiss Inst Environm & Sustainabil Water Resources Unit Joint Res Ctr Via E Fermi 2749 I-21027 Ispra VA Italy Univ Lyon UMR CNRS EVS 5600 ISIG Platform Site ENS Lyon15 Parvis Rene Descartes F-69362 Lyon France

In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of Remote Sensing (RS) data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.g., water channel, unvegetated sediment bars, riparian densely-vegetated units, etc.) and in-stream mesohabitats with a high level of accuracy, respectively of K = 0.91 and K = 0.83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to river science and management, notably for planning and targeting actions.

关键词： hydromorphology object-based classification LiDAR data VHR imagery machine learning riverscape units hierarchical classification

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