Remote Sensing Image Classification Algorithm Based on Rough Set Theory

Dong, Guangjun; Zhang, Yongsheng; Fan, Yong-Hong

doi:10.1007/978-3-540-71441-5_92

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Since RST is useful in analyzing data with insufficient and incomplete knowledge it has been applied in many traditional domains including finance, medicine, telecommunications, vibration analysis, control theory, signal analysis, pattern recognition, and image analysis (Yasdi, 1996;Polkowski and Skowron, 1998;Polkowski et al, 2000;Skowron, 2001;Leung and Li, 2003). The literature has also revealed that RST has been applied to problems of spatial analysis (Bittner, 2001;Bittner and Stell, 2001); spatial classification and uncertainty analysis (Ahlqvist et al, 2000(Ahlqvist et al, , 2003, geo-knowledge discovery (Wang et al, 2001;Beaubouef et al, 2007), remote sensing image classification (Dong et al, 2007), and in the extraction of decision rules in GIS and remote sensing (Berger, 2004;Leung et al, 2007;Bai et al, 2009). Moreover, Cao et al (2009) initially attempted to extract the spatial relationship indicator rules based on RST.…”

Section: Brief Remarks On Rough Set Theory and Its Applicationsmentioning

confidence: 94%

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Cao

et al. 2011

J. Geogr. Sci.

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Spatial relations, reflecting the complex association between geographical phenomena and environments, are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization, an important geographical issue, is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter, the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong, located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan, Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan, Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.

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Section: Brief Remarks On Rough Set Theory and Its Applicationsmentioning

confidence: 94%

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Cao

et al. 2011

J. Geogr. Sci.

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“…Remote sensing image classification is one of the most important application domains techniques, and also a form of showing the property of image objects directly and vividly [17]. The image classification procedure is in either a pixelbased or a region-based approach.…”

Section: Methodsmentioning

confidence: 99%

Beach hydromorphological classification through image classification techniques applied to remotely sensed data

Teodoro¹,

Pais-Barbosa²,

Gomes³

et al. 2009

SPIE Proceedings

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Evaluation of beach hydromorphological behavior and its classification is extremely complex. Several aerial photographs, using visual interpretation on a GIS environment, were previously used on the identification of coastal hydroforms and hydromorphologies, and to classify beach morphological stage in a selected area of the NW Portuguese coast. The goal of this study is to improve and develop new methodologies to identify coastal features and coastal patterns. In order to achieve that, pixel-based classification and object-oriented classification algorithms were employed, with the aim to identify and analyze morphological features and hydrodynamic patterns and to compare these results with the visual interpretation already performed. The dataset is composed by two aerial photographs (1996 and 2001) and one IKONOS-2 image (2004). The supervised classification algorithms presented good results both for aerial photographs and for IKONOS-2 image, demonstrated by its overall accuracy and Kappa coefficient values. For the two aerial photographs the best results were found for the maximum likelihood classifier and for the IKONOS-2 image the best result was archived with the parallelepiped classifier. The object-oriented classification performance for the aerial photographs was very good, identifying the classes of interest. The results obtained with the IKONOS-2 image were worst.

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“…Rough sets theory is a recently developed soft computing technique for handling ambiguity and uncertainty. Dong et al [19] highlights the fundamental theory, nature, and contemporary applications of rough sets. The processing of RSI classification then incorporates the rough set theory.…”

Section: Related Workmentioning

confidence: 99%

A hybrid approach to remote sensing image classification based on pixel density distribution and adaptive neural networks

Singh

2024

IJ-AI

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<span lang="EN-US">The proposed study employed a method for identifying the main contents (category/class) that a Remote Sensing Image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward Artificial Neural Network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing.</span>

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Remote Sensing Image Classification Algorithm Based on Rough Set Theory

Cited by 7 publications

References 4 publications

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Beach hydromorphological classification through image classification techniques applied to remotely sensed data

A hybrid approach to remote sensing image classification based on pixel density distribution and adaptive neural networks

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