Evaluating Object-Based Data Quality Attributes in the Land Cover Map 2000 of the United Kingdom

In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

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COMPARISON OF PIXEL-BASED AND OBJECT-BASED CLASSIFICATION USING PARAMETERS AND NON-PARAMETERS APPROACH FOR THE PATTERN CONSISTENCY OF MULTI SCALE LANDCOVER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-765-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 765-771 ◽

Cited By ~ 2

Author(s):

E. Juniati ◽

E. N. Arrofiqoh

Keyword(s):

Land Cover ◽

Remote Sensing Data ◽

Landsat 8 ◽

Topographic Map ◽

Visual Interpretation ◽

Object Based ◽

Study Analyse ◽

Land Cover Map ◽

Map Scale ◽

Neural Network Classifiers

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

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Land Cover Classification of Complex Agroecosystems in the Non-Protected Highlands of the Galapagos Islands

Remote Sensing ◽

10.3390/rs12010065 ◽

2019 ◽

Vol 12 (1) ◽

pp. 65 ◽

Cited By ~ 2

Author(s):

Francisco J. Laso ◽

Fátima L. Benítez ◽

Gonzalo Rivas-Torres ◽

Carolina Sampedro ◽

Javier Arce-Nazario

Keyword(s):

Land Cover ◽

Invasive Plants ◽

Mixed Forest ◽

High Spectral Resolution ◽

Object Based ◽

Pioneer Plants ◽

Land Cover Map ◽

Aerial Vehicle ◽

Sentinel 2

The humid highlands of the Galapagos are the islands’ most biologically productive regions and a key habitat for endemic animal and plant species. These areas are crucial for the region’s food security and for the control of invasive plants, but little is known about the spatial distribution of its land cover. We generated a baseline high-resolution land cover map of the agricultural zones and their surrounding protected areas. We combined the high spatial resolution of PlanetScope images with the high spectral resolution of Sentinel-2 images in an object-based classification using a RandomForest algorithm. We used images collected with an unmanned aerial vehicle (UAV) to verify and validate our classified map. Despite the astounding diversity and heterogeneity of the highland landscape, our classification yielded useful results (overall Kappa: 0.7, R2: 0.69) and revealed that across all four inhabited islands, invasive plants cover the largest fraction (28.5%) of the agricultural area, followed by pastures (22.3%), native vegetation (18.6%), food crops (18.3%), and mixed forest and pioneer plants (11.6%). Our results are consistent with historical trajectories of colonization and abandonment of the highlands. The produced dataset is designed to suit the needs of practitioners of both conservation and agriculture and aims to foster collaboration between the two areas.

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Record-linkage and capture–recapture analysis to estimate the incidence and completeness of reporting of tuberculosis in England 1999–2002

Epidemiology and Infection ◽

10.1017/s0950268808000496 ◽

2008 ◽

Vol 136 (12) ◽

pp. 1606-1616 ◽

Cited By ~ 32

Author(s):

N. A. H. VAN HEST ◽

A. STORY ◽

A. D. GRANT ◽

D. ANTOINE ◽

J. P. CROFTS ◽

...

Keyword(s):

United Kingdom ◽

Data Quality ◽

Record Linkage ◽

False Positive ◽

Cross Validation ◽

Data Sources ◽

The United Kingdom ◽

Capture Recapture ◽

Log Linear ◽

Completeness Of Reporting

SUMMARYIn 1999 the Enhanced Tuberculosis Surveillance (ETS) system was introduced in the United Kingdom to strengthen surveillance of tuberculosis (TB). The aim of this study was to assess the use of record-linkage and capture–recapture methodology for estimating the completeness of TB reporting in England between 1999 and 2002. Due to the size of the TB data sources sophisticated record-linkage software was required and the proportion of false-positive cases among unlinked hospital-derived TB records was estimated through a population mixture model. This study showed that record-linkage of TB data sources and cross-validation with additional TB-related datasets improved data quality as well as case ascertainment. Since the introduction of ETS observed completeness of notification in England has increased and the results were consistent with expected levels of under-notification. Completeness of notification estimated by a log-linear capture–recapture model was highly inconsistent with prior estimates and the validity of this methodology was further examined.

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A Study on Land Cover Map of UAV Imagery using an Object-based Classification Method

Journal of Korean Society for Geospatial Information System ◽

10.7319/kogsis.2015.23.4.025 ◽

2015 ◽

Vol 23 (4) ◽

pp. 25-33 ◽

Cited By ~ 4

Author(s):

Ji Sun Shin ◽

Tae Ho Lee ◽

Pil Mo Jung ◽

Hyuk Soo Kwon

Keyword(s):

Land Cover ◽

Classification Method ◽

Object Based ◽

Land Cover Map

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From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

Remote Sensing ◽

10.3390/rs13091700 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1700

Author(s):

Dang Hung Bui ◽

László Mucsi

Keyword(s):

Land Use ◽

Random Forest ◽

Land Cover ◽

Decision Rules ◽

Random Forest Classifier ◽

Landsat Images ◽

Object Based ◽

Multi Temporal ◽

Land Cover Map ◽

Land Cover Maps

It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

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