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object-based Detection of Impervious Area in Agriculture Land Using High-Resolution Satellite image

测量工程 2019年 58卷 21-36页

作者： Nurahida Laili 王驥魁

Agricultural land is important for the food security of a nation. However, the total agriculture land area across the world has been decreasing every year due to human activities. Developing technology, especially in businesses and other industries, is resulting in the increased construction of buildings, which then results in loss of land. People build factories, houses, etc. on agricultural areas, which then become impervious areas as the water absorption ability of the ground underneath decreases. A periodical assessments to record the change in the total farmland area need to be carried out. Traditional manual digitation is usually conducted to detect impervious areas in agricultural land. However, this process is laborious. Thus, this study uses an object-based approach that employs high-resolution satellite images to detect the impervious areas. A pan-sharpened Pleiades image with 0.5-meter resolution and four spectral bands were utilized. The HSV (hue-saturation-value) bands derived from the RGB bands were added as object features to extract the impervious area. The spectral feature, i.e., HSV, NDVI, NDWI, the soil extraction algorithms, and the shape feature, i.e., size and compactness, were deployed to extract the impervious area within the agricultural land. An F1-score of 0.70 was obtained from this proposed method. Furthermore, the transferability test was carried out by applying the method to different image size and different image acquisition time. The result shows that the method is stable to process various image scenes.

关键词： image classification image segmentation object-based image analysis Pleiades image

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Automating field boundary delineation with multi-temporal Sentinel-2 imagery

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 167卷 105078-000页

作者： Watkins, Barry Van Niekerk, Adriaan Univ Stellenbosch Dept Geog & Environm Studies Stellenbosch South Africa

Knowledge of the extent and location of cultivated fields are critical for agricultural monitoring, food security planning and commodity trading. The increased observational capacity of remotely sensed data, such as Sentinel-2 imagery, constitutes a major advantage over in situ methods, particularly for agricultural applications that require frequent updates. However, the development of fully automated cultivated field delineation methods remains a challenge. In this study an existing object-based image analysis (OBIA) methodology, called Canny edge-detection in conjunction with watershed segmentation (CEWS), was modified to automatically identify and delineate agricultural fields from multi-temporal Sentinel-2 imagery in five diverse agricultural landscapes. The robustness of the technique was evaluated by comparing its outputs to those of standard perpixel, supervised classifications. Reference field boundaries were manually digitised and used for quantitative accuracy assessments. Area and edge metrics were used to evaluate the accuracy of the extracted boundaries. Results show that, on average, CEWS produced significantly higher field boundary accuracies than the supervised per-pixel approach in terms of both mean absolute error (MAE) (similar to 31 m lower) and overall accuracy (similar to 13.8% higher). The closed field boundaries produced by CEWS, as well as its capability to operate without any a priori knowledge, was found to be its main strengths for integration into operational workflows. However, in complex landscapes (e.g. Swartland and Mooketsi) it was found that some fields had weak boundaries due to the homogeneity between adjacent fields, making edge detection problematic. The incorporation of higher resolution imagery into the edge detection process is proposed, particularly in agricultural areas with very small and irregularly shaped fields. Despite these challenges, it was concluded that the multi-temporal CEWS approach will perform well in most

关键词： Sentinel-2 imagery Edge detection Watershed segmentation object-based image analysis Agricultural field boundary delineation

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Change Detection of High Spatial Resolution images based on Region-Line Primitive Association analysis and Evidence Fusion

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REMOTE SENSING 2019年第21期11卷 2484-2484页

作者： Huang, Jiru Liu, Yang Wang, Min Zheng, Yalan Wang, Jie Ming, Dongping Nanjing Normal Univ Sch Geog Nanjing 210023 Jiangsu Peoples R China 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 State Key Lab Cultivat Base Geog Environm Evolut Nanjing 210023 Jiangsu Peoples R China China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China

Change detection (CD) remains an important issue in remote sensing applications, especially for high spatial resolution (HSR) images, but it has yet to be fully resolved. This work proposes a novel object-based change detection (OBCD) method for HSR images that is based on region-line primitive association analysis and evidence fusion. In the proposed method, bitemporal images are separately segmented, and the segmentation results are overlapped to obtain the temporal region primitives (TRPs). The temporal line primitives (TLPs) are obtained by straight line detection on bitemporal images. In the initial CD stage, Dempster-Shafer evidence theory fuses the multiple items of evidence of the TRPs' spectrum, edge, and gradient changes, and obtains the initial changed areas. In the refining CD stage, the association between the TRPs and their contacting TLPs in the unchanged areas is established on the basis of the region-line primitive association framework, and the TRPs' main line directions (MLDs) are calculated. Some changed TRPs omitted in the initial CD stage are recovered by their MLD changes, thereby refining the initial CD results. Different from common OBCD methods, the proposed method considers the change evidence of TRPs' internal and boundary information simultaneously via information complementation between TRPs and TLPs. The proposed method can significantly reduce missed alarms while maintaining a low level of false alarms in OBCD, thereby improving total accuracy. In our experiments, our method is superior to common CD methods, including change vector analysis (CVA), PCA-k-means, and iterative reweighted multivariate alteration detection (IRMAD), in terms of overall accuracy, missed alarms, and Kappa coefficient.

关键词： object-based image analysis change detection high spatial resolution imagery evidence theory region-line primitive association framework

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Automated identification of intensive animal production locations from aerial photography

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AUSTRALIAN VETERINARY JOURNAL 2018年第9期96卷 323-331页

作者： Sheffield, K. J. Hunnam, J. C. Cuzner, T. N. Morse-McNabb, E. M. Sloan, S. M. Nunan, J. Smith, J. Harvey, W. Lewis, H. Agr Victoria Res AgriBio Dept Econ Dev Jobs Transport & Resources 5 Ring Rd Bundoora Vic 3083 Australia Agr Victoria Energy & Resources Dept Econ Dev Jobs Transport & Resources Attwood Vic Australia Agr Victoria Res Dept Econ Dev Jobs Transport & Resources Epsom Vic Australia Agr Victoria Res Dept Econ Dev Jobs Transport & Resources Tatura Vic Australia

objective Successful control of an emergency animal disease outbreak requires the timely and accurate identification of properties of interest. The identification of commercial piggeries within study areas in the Goulburn-Murray Irrigation District in Victoria, Australia, is used to demonstrate the innovative application of object-based image analysis (OBIA) techniques for the identification of intensive animal production land uses, to improve the accuracy of existing datasets. Methods Characteristics of infrastructure and landscape features were combined to form a commercial piggery identification algorithm. These criteria were applied to recent aerial photography that had been classified using OBIA techniques. The results were then compared with three datasets containing known commercial piggery locations and visually checked by roadside surveys. Results The OBIA technique identified 21 potential piggery locations across three study areas, 14 of which were identified in existing databases. Of the 7 additional sites, 4 were dairy properties, 1 was a cropping and sheep property and 2 were previously undocumented piggery locations. Conclusions The OBIA approach has potential of OBIA for identifying the locations of commercial piggeries. Further development and testing will determine how generic this approach is in terms of industry type and operation size. The method described is cost-effective, automated and repeatable, and could be used to regularly update existing databases by analysing newly acquired aerial imagery to identify possible land use changes. This would improve the reliability of currently available data and increase the effectiveness of a biosecurity response during an emergency animal disease outbreak.

关键词： biosecurity commercial piggeries mapping object-based image analysis remote sensing Victoria

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Role of geospatial technology in understanding urban green space of Kalaburagi city for sustainable planning

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URBAN FORESTRY & URBAN GREENING 2019年 46卷 126450-000页

作者： Shekhar, Sulochana Aryal, Jagannath Cent Univ Tamil Nadu Dept Geog Neelakudy Tamil Nadu India Univ Tasmania Discipline Geog & Spatial Sci Hobart Tas Australia

Mapping and monitoring of the urban green space (UGS) are necessary for improving the quality of urban life. Mapping the UGS is the first step in sustainable urban planning because comprehensive information on vegetation in cities is lacking. Very high- resolution imagery and object-based image analysis (OBIA) offer a viable semi-automatic solution in mapping the UGS in greater detail. In this study, GeoEye image is used to map the spatial distribution of UGS in Kalaburagi, India. Normalised Difference Vegetation Index (NDVI) values are used including fuzzy rule sets in classifying the image in OBIA environment. A new urban green space index (UGSI) is developed to visualize the density of greenness in Kalaburagi city. The analysis is carried out at two levels considering the granularity. The first Meso-level analysis is at the ward level of Kalaburagi city. The second micro-level analysis is at 200 m x 200 m grid zones level. The UGS extraction results are highly accurate (> 90%). The results are crucial to urban planners as the results are critical to the Kalaburagi city where 22 administrative wards out of 55in Kalaburagi have less than 10% of UGS and 25 out of 55 wards have less than 9 m(2) Per Capita Green Space (PCGS). Priority has to be given to these areas to build a healthy city through green planning.

关键词： Urban green space object-based image analysis Normalised difference vegetation index Kalaburagi Per capita green space Green planning

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object-based method for optical and SAR images change detection

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JOURNAL OF ENGINEERING-JOE 2019年第21期2019卷 7410-7414页

作者： Wan, Ling Zhang, Tao You, Hongjian Key Lab Technol Geospatial Informat Proc & Applic Beijing 100190 Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China

This study introduces an automatic method for change detection of multi-sensor remote-sensing images (e.g. optical and synthetic aperture radar (SAR) images). As object-based image analysis can effectively reduce the spurious changes and the sensitivity to registration, first, multi-date segmentation is employed to generate homogeneous image objects in spectral, spatial, and temporal, in order to weak the intensity variation effects of multi-sensor images. Then, modified fuzzy c-means (FCM) algorithms are employed to preliminarily classify optical and SAR images, and a criterion is defined using membership values of parcels to select the sample parcels for each class and image. Finally, a change detection principle, which takes statistical properties as the feature space, is introduced to detect changes between multi-sensor images. The experiment results verify that the proposed method is able to cope with optical and SAR images change detection.

关键词： remote sensing synthetic aperture radar geophysical image processing image segmentation feature extraction radar imaging object-based image analysis synthetic aperture radar optical aperture radar multisensor remote-sensing images SAR images change detection optical images change detection change detection principle optical SAR images multisensor images homogeneous image objects spurious changes

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object-based random forest classification of Landsat ETM plus and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2018年 66卷 46-55页

作者： Melville, Bethany Lucieer, Arko Aryal, Jagannath Univ Tasmania Sch Land & Food Churchill Ave Hobart Tas 7000 Australia

This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed;a Landsat ETM + scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k = 50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be us

关键词： Native grasslands Random forest k-fold cross-validation object-based image analysis

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Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2018年 73卷 170-178页

作者： Phiri, Darius Morgenroth, Justin Xu, Cong Hermosilla, Txomin Univ Canterbury New Zealand Sch Forestry Christchurch New Zealand Univ British Columbia Fac Forestry Vancouver BC Canada

The application of Landsat satellite imagery in land cover classification is affected by atmospheric and topographic errors, which have led to the development of different correction methods. In this study, moderate resolution atmospheric transmission (MODTRAN) and dark object subtraction (DOS) atmospheric corrections, and cosine topographic correction were evaluated individually and combined in a heterogeneous landscape in Zambia. These pre-processing methods were tested using a combination of object-based image analysis (OBIA) and Random Forests (RF) non-parametric classifier (hereafter referred to as OBIA-RF). This assessment aimed at understanding the combined effects of different pre-processing methods and the OBIA-RF classification method on the accuracy of Landsat operational land (OLI-8) imagery with different spatial resolutions. Here, we used pansharpened and standard Landsat OLI-8 images with 15 and 30 m spatial resolutions, respectively. The results showed that non pre-processed images reached a classification accuracy of 68% for pansharpened and 66% for standard Landsat OLI-8. Classification accuracy improved to 93% (pansharpened) and 86% (standard) when combined MODTRAN and cosine topographic correction pre-processing were applied. The results highlight the importance of pansharpening, as well as atmospheric and topographic corrections for Landsat OLI-8 imagery, when used as input in OBIA classification with the RF classifier.

关键词： Remote sensing object-based image analysis Satellite imagery Pansharpening Atmospheric correction Topographic correction Landsat Random forests

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Detecting plague-host abundance from space: Using a spectral vegetation index to identify occupancy of great gerbil burrows

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2018年 64卷 249-255页

作者： Wilschut, Liesbeth I. Heesterbeek, Johan A. P. Begon, Mike de Jong, Steven M. Ageyev, Vladimir Laudisoit, Anne Addink, Elisabeth A. Univ Utrecht Dept Phys Geog Heidelberglaan 2POB 80115 NL-3508 TC Utrecht Netherlands Univ Utrecht Fac Vet Med Yalelaan 7 NL-3584 CL Utrecht Netherlands Univ Liverpool Ecol Evolut & Genom Infect Dis Res Grp Inst Integrat Biol Liverpool Merseyside England M Aikimbayevs Kazakh Sci Ctr Quarantine & Zoonot Antiplague Inst 14 Kapalskaya St Alma Ata 050074 Kazakhstan Univ Antwerp Evolutionary Biol Grp 171 Groenenborgerlaan B-2020 Antwerp Belgium

In Kazakhstan, plague outbreaks occur when its main host, the great gerbil, exceeds an abundance threshold. These live in family groups in burrows, which can be mapped using remote sensing. Occupancy (percentage of burrows occupied) is a good proxy for abundance and hence the possibility of an outbreak. Here we use time series of satellite images to estimate occupancy remotely. In April and September 2013, 872 burrows were identified in the field as either occupied or empty. For satellite images acquired between April and August, 'burrow objects' were identified and matched to the field burrows. The burrow objects were represented by 25 different polygon types, then classified (using a majority vote from 10 Random Forests) as occupied or empty, using Normalized Difference Vegetation Indices (NDVI) calculated for all images. Throughout the season NDVI values were higher for empty than for occupied burrows. Occupancy status of individual burrows that were continuously occupied or empty, was classified with producer's and user's accuracy values of 63 and 64% for the optimum polygon. Occupancy level was predicted very well and differed 2% from the observed occupancy. This establishes firmly the principle that occupancy can be estimated using satellite images with the potential to predict plague outbreaks over extensive areas with much greater ease and accuracy than previously.

关键词： Plague Yersinia pestis Infectious disease Great gerbil Population abundance NDVI object-based image analysis Segmentation Random Forest

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Homisland-IO: Homogeneous Land Use/Land Cover over the Small Islands of the Indian Ocean

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DATA 2019年第2期4卷 82页

作者： Revillion, Christophe Attoumane, Artadji Herbreteau, Vincent Univ La Reunion UMR 228 Espace Dev IRD UAUGUMUR 40 Ave Soweto F-97410 St Pierre St Pierre & Miq France Inst Rech Dev UMR 228 Espace Dev UR UAUGUM 40 Ave Soweto F-97410 St Pierre St Pierre & Miq France Inst Rech Dev UMR 228 Espace Dev UA UGUMUR 5 Preah Monivong Blvd 93 Phnom Penh 12100 Cambodia

Many small islands are located in the southwestern Indian Ocean. These islands have their own environmental specificities and very fragmented landscapes. Generic land use products developed from low and medium resolution satellite images are not suitable for studying these small territories. This is why we have developed a land use/land cover product, called Homisland-IO, based on remote sensing processing on high spatial resolution satellite images acquired by SPOT 5 satellite between December 2012 and July 2014. This product has been produced using an object-based classification process. The overall accuracy of the product is 86%. Homisland-IO is freely accessible through a web portal and is thus available for future use.

关键词： land cover remote sensing object-based image analysis small island Indian Ocean

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