检索结果-内蒙古大学图书馆

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

REMOTE SENSING 2017年第3期9卷 239-239页

作者： Wu, Mingquan Yang, Chenghai Song, Xiaoyu Hoffmann, Wesley Clint Huang, Wenjiang Niu, Zheng Wang, Changyao Li, Wang Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci POB 971820 Datun Rd Beijing 100101 Peoples R China USDA ARS Aerial Applicat Technol Res Unit 3103 F&B Rd College Stn TX 77845 USA Beijing Acad Agr & Forestry Sci Beijing Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Lab Digital Earth Sci Beijing 100094 Peoples R China

Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop height for the identification of crop types (mainly corn, cotton, and sorghum). The crop height was extracted by subtracting the DSM derived during the crop growing season from that derived after the crops were harvested. Then, the crops were identified from four-band aerial imagery (blue, green, red, and near-infrared) and the crop height, using an object-based classification method and a maximum likelihood method. The results showed that the extracted crop height had a very high linear correlation with the field measured crop height, with an R-squared value of 0.98. For the object-based method, crops could be identified from the four-band airborne imagery and crop height, with an overall accuracy of 97.50% and a kappa coefficient of 0.95, which were 2.52% and 0.04 higher than those without crop height, respectively. When considering the maximum likelihood, crops could be mapped from the four-band airborne imagery and crop height with an overall accuracy of 78.52% and a kappa coefficient of 0.67, which were 2.63% and 0.04 higher than those without crop height, respectively.

关键词： aerial imagery crop mapping consumer-grade cameras crop height object-based classification

来源：评论

学校读者我要写书评

暂无评论

"Kill Two Birds with One Stone": Urban Tree Species classification Using Bi-Temporal Pleiades Images to Study Nesting Preferences of an Invasive Bird

引用

REMOTE SENSING 2017年第9期9卷 916-916页

作者： Le Louarn, Marine Clergeau, Philippe Briche, Elodie Deschamps-Cottin, Magali Aix Marseille Univ IRD LPED Marseille France Museum Natl Hist Nat UMR CESCO 43 Rue BuffonCP135 F-75005 Paris France

This study presents the results of object-based classifications assessing the potential of bi-temporal Pleiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was performed based solely on a summer Pleiades image (acquired on 28 July 2015) and the second classification based on bi-temporal Pleiades images (a spring image acquired on 24 March 2016 and the summer image). An ensemble of spectral and textural features was extracted from both images and two machine-learning classifiers were used, Random Forest (RF) and Support Vector Machine (SVM). Regardless of the classifiers, model results suggest that classification based on bi-temporal Pleiades images produces more satisfying results, with an overall accuracy 11.5-13.9% higher than classification using the single-date image. Textural and spectral features extracted from the blue and the NIR bands were consistently ranked among the most important features. Regardless of the classification scheme, RF slightly outperforms SVM. RF classification using bi-temporal Pleiades images allows identifying 98.5% of the tree species used by the ring-necked parakeet for nesting, highlighting the promising value of remote sensing techniques to assess the ecological requirements of fauna in urban areas.

关键词： urban trees Pleiades random forest support vector machine object-based classification Psittacula krameri

来源：评论

学校读者我要写书评

暂无评论

Assessing the Potential of Sentinel-2 and Pleiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

引用

REMOTE SENSING 2017年第1期9卷 74-74页

作者： Ng, Wai-Tim Rima, Purity Einzmann, Kathrin Immitzer, Markus Atzberger, Clement Eckert, Sandra Univ Nat Resources & Life Sci Vienna BOKU Inst Surveying Remote Sensing & Land Informat Peter Jordan Str 82 A-1190 Vienna Austria Kenya Forestry Res Inst KEFRI Baringo Sub Ctr POB 57-30403 Marigat Kenya Chuka Univ Fac Arts & Humanities Dept Geog POB 109-60400 Chuka Kenya Univ Bern Ctr Dev & Environm Hallerstr 10 CH-3012 Bern Switzerland

Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopiscompeting for nutrients and waterhas the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pleiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. classification results were validated by comparing the outputs to independent reference data of test sites from the Woody Weeds project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pleiades data (OOB accuracy 0.83 and independent reference accuracy 0.87-0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80-0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pleiades, the free of cha

关键词： Prosopis Vachellia object-based classification Random Forest (RF) Sentinel-2 Pleiades invasive plant species East Africa

来源：评论

学校读者我要写书评

暂无评论

object-based Image Analysis for Coral Reef Benthic Habitat Mapping with Several classification Algorithms

引用

Procedia Environmental Sciences 2015年 24卷 222-227页

作者： Nurhalis Wahidin Vincentius P. Siregar Bisman Nababan Indra Jaya Sam Wouthuyzen Marine Technology Graduate Program Faculty of Fisheries and Marine Sciences Bogor Agricultural University Bogor Indonesia Faculty of Fisherie and Marine Science Khiarun University Ternate Indonesia Department of Marine Science and Technology Bogor Agricultural University Bogor Indonesia Oceanographic Research Center Indonesian Institute of Science Jakarta Indonesia

object-based classification technique (OBIA) in Landsat image analysis is assumed to be able to increase classification accuracy result compared with a pixel-based classification technique. Most of the previous research was conducted using conventional (pixel-based) classification technique such as maximum likelihood algorithm. The objective of this research was to determine accuracy values of coral reef habitat classification based on OBIA algorithms such as Support Vector Machine (SVM), Random Tree, Decision Tree (DT), Bayesian, and k-Nearest Neighbour (KNN). The Landsat 8 OLI satellite imagery acquired on 17 October 2013 on coral reef area in Morotai island, North Maluku Province, Indonesia was analysed in this study. Field data was obtained in October 2012 using transect photo technique. The field data was then classified employing OBIA classification within 7 (seven) classes i.e., sand-rubble, rubble, coral, sand, sand-algae, seagrass, and rubble-sand. Results showed that the highest values of overall accuracy and Kappa on coral reef habitat mapping was produced by SVM algorithm which reached 73% and 0.64 followed by RT, KNN, Bayesian, and DT algorithms of 68% and 0.59, 67% and 0.56, 66% and 0.53, and 56% and 0.46, respectively. Those results showed that the classification methods based OBIA approach within > 6 classes produced better accuracy than the pixel based classification technique.

关键词： object-based classification pixel-based classification coral reef benthic habitat overall accuracy Kappa Morotai island

来源：评论

学校读者我要写书评

暂无评论

In-Season Crop Mapping with GF-1/WFV Data by Combining object-based Image Analysis and Random Forest

引用

REMOTE SENSING 2017年第11期9卷 1184-1184页

作者： Song, Qian Hu, Qiong Zhou, Qingbo Hovis, Ciara Xiang, Mingtao Tang, Huajun Wu, Wenbin Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Minist Agr Key Lab Agr Remote Sensing AGRIRS Beijing 100081 Peoples R China Michigan State Univ Ctr Syst Integrat & Sustainabil E Lansing MI 48824 USA

Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

关键词： crop mapping in-season object-based classification Random Forest

来源：评论

学校读者我要写书评

暂无评论

object-based classification USING ULTRACAM-D IMAGES FOR TREE SPECIES DISCRIMINATION (CASE STUDY: HYRCANIAN FOREST-IRAN)

OBJECT-BASED CLASSIFICATION USING ULTRACAM-D IMAGES FOR TREE...

引用

Conference on Geographic object-based Image Analysis (GEOBIA)

作者： Rafieyan, O. Darvishsefat, A. A. Babaii, S. Mataji, A. Islamic Azad Univ Tabriz Branch Tehran Iran Univ Tehran Nat Resources Fac Dept Forestry Karaj Iran Islamic Azad Univ Sci & Res Branch Tehran Iran

This research has been conducted to evaluate the proposed object-based method with high spatial resolution airborne remote sensing data in tree species identification and mapping. It has been done in 2 areas of total 10 separate areas;include the disturbed natural broadleaved forest and the broadleaved and coniferous mixed forestation in the Northern forests of Iran. After pre-processing the imagery a trial and error method was employed to reach the ideal segmentation results. Subsequent to class definition, sample objects were selected as representative of defined classes and NN classifier was accomplished using integration of a broad spectrum of different object features. Accuracy assessment of the produced maps, comparing with field reference data shows the overall accuracies and Kappa statistics of 0.79, 0.61 (Area1) and 0.76, 0.69 (Area2) respectively. Relatively low accuracy in both areas demonstrated that the standalone optical remote sensing methods are insufficient for classification of such complex forest structures.

关键词： object-based classification Image segmentation Ground truth UltraCamD High spatial resolution

来源：评论

学校读者我要写书评

暂无评论

PIXEL VS object-based IMAGE classification TECHNIQUES FOR LIDAR INTENSITY DATA

PIXEL VS OBJECT-BASED IMAGE CLASSIFICATION TECHNIQUES FOR LI...

引用

ISPRS Workshop on Laser Scanning

作者： El-Ashmawy, Nagwa Shaker, Ahmed Yan, Wai Yeung Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada Natl Water Res Ctr Survey Res Inst Cairo Egypt

Light Detection and Ranging (LiDAR) systems are remote sensing techniques used mainly for terrain surface modelling. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which provides high separability in the reflected energy by the target. This phenomenon is investigated to use the LiDAR intensity data for land-cover classification. The goal of this paper is to investigate and evaluates the use of different image classification techniques applied on LiDAR intensity data for land cover classification. The two techniques proposed are: a) Maximum likelihood classifier used as pixel-based classification technique;and b) Image segmentation used as object-based classification technique. A study area covers an urban district in Burnaby, British Colombia, Canada, is selected to test the different classification techniques for extracting four feature classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas, from the LiDAR intensity data. Generally, the results show that LiDAR intensity data can be used for land cover classification. An overall accuracy of 63.5% can be achieved using the pixel-based classification technique. The overall accuracy of the results is improved to 68% using the object-based classification technique. Further research is underway to investigate different criteria for segmentation process and to refine the design of the object-based classification algorithm.

关键词： LiDAR intensity data object-based classification decision tree

来源：评论

学校读者我要写书评

暂无评论

Accurate determination of surface reference data in digital photographs in ice-free surfaces of Maritime Antarctica

引用

SCIENCE OF THE TOTAL ENVIRONMENT 2016年 573卷 290-302页

作者： Pina, Pedro Vieira, Goncalo Bandeira, Lourenco Mora, Carla Univ Lisbon Inst Super Tecn CERENA Ave Rovisco Pais P-1049001 Lisbon Portugal Univ Lisbon CEG Inst Geog & Ordenamento Terr Rua Branca Edmee Marques P-1600276 Lisbon Portugal

The ice-free areas of Maritime Antarctica show complex mosaics of surface covers, with wide patches of diverse bare soils and rock, together with various vegetation communities dominated by lichens and mosses. The micro scale variability is difficult to characterize and quantify, but is essential for ground-truthing and for defining classifiers for large areas using, for example high resolution satellite imagery, or even ultra-high resolution unmanned aerial vehicle (UAV) imagery. The main objective of this paper is to verify the ability and robustness of an automated approach to discriminate the variety of surface types in digital photographs acquired at ground level in ice-free regions of Maritime Antarctica. The proposed method is based on an object-based classification procedure built in two main steps: first, on the automated delineation of homogeneous regions (the objects) of the images through the watershed transform with adequate filtering to avoid an over-segmentation, and second, on labelling each identified object with a supervised decision classifier trained with samples of representative objects of ice-free surface types (bare rock, bare soil, moss and lichen formations). The method is evaluated with images acquired in summer campaigns in Fildes and Barton peninsulas (King George Island, South Shetlands). The best performances for the datasets of the two peninsulas are achieved with a SVM classifier with overall accuracies of about 92% and kappa values around 0.89. The excellent performances allow validating the adequacy of the approach for obtaining accurate surface reference data at the complete pixel scale (sub-metric) of current very high resolution (VHR) satellite images, instead of a common single point sampling. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Image analysis Segmentation object-based classification Ice-free surfaces Polar regions

来源：评论

学校读者我要写书评

暂无评论

Mapping Changes in Land Cover Composition and Pattern for Comparing Mediterranean Rangeland Restoration Alternatives

引用

LAND DEGRADATION & DEVELOPMENT 2016年第3期27卷 671-681页

作者： Fava, Francesco Pulighe, Giuseppe Monteiro, Antonio T. Univ Milano Bicocca Dept Earth & Environm Sci Remote Sensing Environm Dynam Lab Piazza Sci 1 I-20126 Milan Italy Consiglio Ric Agr & Anal Econ Agr Via Nomentana 41 Rome Italy Univ Porto Res Ctr Biodivers & Genet Resources CIBIO Biol Conservat Grp Rua Campo Alegre 823 P-4100 Oporto Portugal

The limited amount of studies addressing the long-term effectiveness of restoration actions to combat land degradation is a constraint for current landscape restoration planning and implementation. Remote sensing data provide a unique opportunity for gathering information on the spatial and temporal variability of restoration processes and may contribute to fill this knowledge gap. The aim of this study is to compare different management actions undertaken in Mediterranean degraded rangelands (*** pine plantations, thinned pine plantations, protection and passive management) by analysing landscape composition and pattern change assessed from object-based classification of aerial photograph of 1954, 1977 and 2006. Results show a clear link between landscape composition and structure and the different restoration actions, allowing comparison between their spatial and temporal dynamics. All restoration actions promoted an increase in woody vegetation cover compared with non-restored areas. However, only protection and passive management favoured the restoration of dense, complex and well-connected patches of native Mediterranean maquis. The proposed methodology is discussed with respect to its applicability for future Mediterranean landscape restoration planning in a wider geographic and ecological perspective. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： restoration land degradation aerial photographs object-based classification spatial pattern metrics Mediterranean

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：