Comparison of Pixel-Based and Object-Oriented Classification Methods for Extracting Built-Up Areas in a Coastal Zone

Kefi, Chayma; Mabrouk, Amina; Ismail, Haythem O.

doi:10.1007/978-3-030-72896-0_76

Cited by 4 publications

(6 citation statements)

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“…Pixel-based classification methods automatically categorize all pixels in an image into land cover classes fundamentally based on spectral similarities (Qian et al, 2007;Weng, 2012). These types of classifiers develop a signature by summing up all pixels.…”

Section: Pixel-based Image Classification Methodsmentioning

confidence: 99%

“…Whereas in the case of supervised image classification the analyst has previous knowledge about pixels to generate representative parameters for each land cover class of interest. The Maximum Likelihood classification, under the category of supervised classification, which is the most widely, used per-pixel method by taking in to account spectral information of land cover classes (Qian et al, 2007). Although pixel based classification methods have been widely accepted and applicable, however, there are limitations in including spatial pattern during classification.…”

Section: Pixel-based Image Classification Methodsmentioning

confidence: 99%

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Land use Change Detection Methodologies and Techniques for Remotely Sensed Images

2024

IJFMR

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The increasing land use changes leading to urban expansion have attracted the researches. Urbanization has become a recent growing trend in many cities around the world. In most cases to estimate the urban expansion with the conventional survey and mapping techniques is expensive and time consuming. Remote sensing is increasingly used for detection and analysis of urban expansion since it is cost effective and technologically efficient. This paper aims to give the taxonomy of various methods and techniques to analyze the land use change detection through remote sensing images in an effective manner. One of the most important functions of remote sensing data is Land Use and Land Cover maps managed through image classification. This describes the various classifiers, software and image digitization techniques that are used in estimating the urban changes. Wide database of images has been used to test the algorithms. Hence, an in-depth study of existing work related to land use change detection will help to accelerate research in the field of urban change. This paper presents a systematic analysis of work done in land use change detection area. We also present a Comparison of existing methods and techniques used for land use change detection. Based on this analysis, we finally presented a few future research directions related to land use change detection through remote sensing images, which will be reduce uncertainties in the image-processing chain to improve classification accuracy.

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Section: Pixel-based Image Classification Methodsmentioning

confidence: 99%

Section: Pixel-based Image Classification Methodsmentioning

confidence: 99%

Land use Change Detection Methodologies and Techniques for Remotely Sensed Images

2024

IJFMR

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“…The process involves selecting suitable pixels to symbolize each target class and executing a solitary classification algorithm to allocate those classes to the pixels in the image. Most published supervised algorithms classify data using maximum likelihood [8]. The two most prevalent methods for pixelbased categorization are Maximum Likelihood Classification and ISO Clustering [9].…”

Section: Introductionmentioning

confidence: 99%

“…Image segmentation techniques have three categories: thresholding/grouping, region, and edge [9,11,14]. Classification by maximum likelihood is the most often used supervised technique in the literature [8]. On this principle, supervised Classification is built.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Al-Zubaidi,

Al Khafaji

2024

WJCMS

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Remote sensing, specifically satellite imagery, is gaining prominence in computer science nowadays, in the era of artificial intelligence, in an attempt to deliver more precise information. The satellite images of Earth are gathered, evaluated, and processed for use in civil and military applications with a military aim. Satellite images do have a wide range of services. The areas of study of agriculture, fishery, oceanography, and meteorology include geology, biodiversity, cartography, land use planning, and armed conflict. Transformation is the goal of the categorization of satellite images. Transformation of satellite images into information that can be used rather than having an image of a location. This paper classified a scene of the Landsat-8 satellites with specifications (Path=168 and Row=38). This scene was classified into four categories (Water, Vegetation, bare land, and Build-up) based on the unsupervised classification method (ISO Clustering). The ISO Clustering method is found in the Arc Map program. The results regarding classification accuracy are a good percentage compared to unsupervised Classification.

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“…15 The object-oriented approach treats an image as a set of significant objects, which requires spatial, spectral, and texture characteristics. 16,17 These requirements create difficulty in optimizing segmentation parameters that hinders the application of object-oriented classification to a large area. 9 Although, SMA-based methods have proven useful for handling the mixed-pixel problems in medium resolution imagery, built-up or impervious surface area (ISA), are commonly overestimated in areas with low-density urban features and underestimated in high-density urban areas.…”

Section: Introductionmentioning

confidence: 99%

Spectral index to improve the extraction of built-up area from WorldView-2 imagery

Adeyemi

Ramoelo

Cho

et al. 2021

J. Appl. Rem. Sens.

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Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data are the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 (WV-2) to improve built-up material mapping irrespective of material type, age, and color. The new index was derived from spectral bands such as green, red edge, NIR1, and NIR2 bands that profoundly explain the variation in built-up areas on WV-2 image. The result showed that NBEI improves the extraction of built-up areas with high accuracy [area under the receiver operating characteristic curve, ðAUROCÞ ¼ ∼0.82] compared to the existing indices such as built-up area index (AUROC ¼ ∼0.73), built-up spectral index (AUROC ¼ ∼0.78), red edge/green index (AUROC ¼ ∼0.71) and WorldView-Built-up Index (WV-BI) (AUROC ¼ ∼0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material-specific, and would be necessary for urban area mapping.

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Comparison of Pixel-Based and Object-Oriented Classification Methods for Extracting Built-Up Areas in a Coastal Zone

Cited by 4 publications

References 0 publications

Land use Change Detection Methodologies and Techniques for Remotely Sensed Images

Land use Change Detection Methodologies and Techniques for Remotely Sensed Images

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Spectral index to improve the extraction of built-up area from WorldView-2 imagery

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