Optimizing classification performance in an object-based very-high-resolution land use-land cover urban application

2017 ◽  
Author(s):  
Stefanos Georganos ◽  
Tais Grippa ◽  
Sabine G. Vanhuysse ◽  
Eléonore Wolff ◽  
Michal Shimoni ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Classification Performance ◽  
Land Use Land Cover ◽  
Object Based ◽  
Very High

Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting

2018 ◽  
Vol 15 (4) ◽  
pp. 607-611 ◽  
Author(s):  
Stefanos Georganos ◽  
Tais Grippa ◽  
Sabine Vanhuysse ◽  
Moritz Lennert ◽  
Michal Shimoni ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Gradient Boosting ◽  
Land Use Land Cover ◽  
Urban Classification ◽  
Object Based ◽  
Extreme Gradient Boosting ◽  
Very High

scholarly journals URBAN VEGETATION CLASSIFICATION WITH NDVI THRESHOLD VALUE METHOD WITH VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 237-240 ◽  
Author(s):  
H. Hashim ◽  
Z. Abd Latif ◽  
N. A. Adnan
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Urban Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Vegetation ◽  
Land Use Land Cover ◽  
Sensing Data ◽  
Very High

Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.

scholarly journals Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3120 ◽  
Author(s):  
Guoyin Cai ◽  
Huiqun Ren ◽  
Liuzhong Yang ◽  
Ning Zhang ◽  
Mingyi Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Classification Scheme ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Large Urban Area ◽  
Object Based ◽  
Medium Resolution ◽  
Very High

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

scholarly journals OBJECT ORIENTED CLASSIFICATION AND FEATURE EXTRACTION FOR PARTS OF EAST DELHI USING HYBRID APPROACH

2018 ◽  
Vol XLII-5 ◽  
pp. 749-754
Author(s):  
P. Kumar ◽  
S. Ravindranath ◽  
K. G. Raj
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Master Plans ◽  
Very High

<p><strong>Abstract.</strong> Rapid urbanization of Indian cities requires a focused attention with respect to preparation of Master Plans of cities. Urban land use/land cover from very high resolution satellite data sets is an important input for the preparation of the master plans of the cities along with extraction of transportation network, infrastructure details etc. Conventional classifiers, which are pixel based do not yield reasonably accurate urban land use/land cover classification of very high resolution satellite data (usually merged images of Panchromatic &amp;amp; Multispectral). Object Based Image Classification techniques are being used to generate urban land use maps with ease which is GIS compatible while using very high resolution satellite data sets. In this study, Object Based Image Analysis (OBIA) has been used to create broad level urban Land Use / Land Cover (LU/LC) map using high resolution ResourceSat-2 LISS-4 and Cartosat-1 pan-sharpened image on the study area covering parts of East Delhi City. Spectral indices, geometric parameters and statistical textural methods were used to create algorithms and rule sets for feature classification. A LU/LC map of the study area comprising of 4 major LU/LC classes with its main focus on separation of barren areas from built up areas has been attempted. The overall accuracy of the result obtained is estimated to be approximately 70%.</p>

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