Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

2012 ◽  
Vol 33 (19) ◽  
pp. 5973-5995 ◽  
Author(s):  
Carolina Moutinho Duque de Pinho ◽  
Leila Maria Garcia Fonseca ◽  
Thales Sehn Korting ◽  
Cláudia Maria de Almeida ◽  
Hermann Johann Heinrich Kux
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Land Cover ◽  
Urban Environment ◽  
Land Cover Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
High Resolution Images

scholarly journals Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1133 ◽  
Author(s):  
Mark Randall ◽  
Rasmus Fensholt ◽  
Yongyong Zhang ◽  
Marina Bergen Jensen
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Supplementary Data ◽  
Catchment Scale ◽  
Hydrologic Modelling ◽  
Sponge City ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

China’s Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation.

scholarly journals Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province

AGRIFOR ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Agus Sofyan
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Aerial Photo ◽  
Recording Time ◽  
Object Based Image Analysis ◽  
Optical Sensitivity ◽  
East Kalimantan ◽  
Object Based ◽  
Open Land

Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) technique and calculate the accuracy and accuracy, land cover classification by using Objeck Based Image (OBIA) analysis through examination of field conditions.classifying land cover, the classification includes shrubs, young shrubs, plantations (oil palms), shrubs, mines, open land, roads and water bodies with Accuracy of Overcome 0.86.

scholarly journals Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

2017 ◽  
Vol 9 (9) ◽  
pp. 939 ◽  
Author(s):  
Yaohuan Huang ◽  
Chuanpeng Zhao ◽  
Haijun Yang ◽  
Xiaoyang Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Image Analysis ◽  
Feature Selection ◽  
Land Cover ◽  
Sample Size ◽  
Land Cover Classification ◽  
High Dimensionality ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover

2020 ◽  
Vol 12 (7) ◽  
pp. 1089
Author(s):  
Lesiba Thomas Tsoeleng ◽  
John Odindi ◽  
Paidamwoyo Mhangara
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Land Cover Classification ◽  
Urban Land ◽  
Analysis Techniques ◽  
Urban Land Cover ◽  
Image Analysis Techniques

Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques

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