An evaluation of fuzzy classifications from IRS 1C LISS III imagery: A case study

Shalan, Mohamed; Arora, Manoj K.; Ghosh, S. K.

doi:10.1080/0143116031000094791

Cited by 35 publications

(15 citation statements)

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“…For example, textural features, as proposed above, were used to improve the urban areas and land cover classification (Shaban and Dikshit, 2001;Rao et al, 2002;Chen et al, 2004); e.g. the artificial neural networks (ANN) was efficiently used in land cover classification (Kavzoglu and Mather, 2003); "fuzzy classification" was efficient in decreasing the mix-pixel problem (Shalan et al, 2003), and the knowledge-based system (KBS), especially, incorporating GIS, plays an important role because it is capable of managing different sources of data (Stefanov, 2001;Daniels, 2006;Lu and Weng, 2006;Alaaddin, 2008).…”

Section: Introductionmentioning

confidence: 99%

Textural and knowledge-based lithological classification of remote sensing data in Southwestern Prieska sub-basin, Transvaal Supergroup, South Africa

Li¹,

Frei

Altermann

2011

Journal of African Earth Sciences

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TM data and other medium spatial resolution satellite data are used in geological and lithologicalmineralogical classification on regular basis, although their usefulness is limited because of relatively coarse spectral resolution. In this contribution, we provide an example for the application of TM data for classification of rocks and minerals within the Neoarchean sedimentary and volcanic basin of Griqualand West, South Africa. An improved methodology is introduced that results in significantly higher classification accuracy. The TM multispectral image and Principal Component analysis (PCA) image of the test area were individually combined with textural features and then classified individually using a Maximum-Likelihood supervised classification (MLC). Subsequently, the two classified images were integrated, compared and re-classified in a knowledge-based system (KBS) using the generalized supplementary geological map on 1:250000 scale. With this method, the accuracy was improved from 54.3% to 83.2%, when compared to the former supervised classification.The method integrates the spectral and textural features, greatly contributing to the precision of the lithological classification, mapping and prospecting, in extensive areas where field work is limited by time and cost constrains.1

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Section: Introductionmentioning

confidence: 99%

Textural and knowledge-based lithological classification of remote sensing data in Southwestern Prieska sub-basin, Transvaal Supergroup, South Africa

Li¹,

Frei

Altermann

2011

Journal of African Earth Sciences

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“…The classified pixel is either completely belongs to a class or not. This is called hard classification [21]. Although in real world the pixel has some spatial resolution and can cover a mixture of two or more class features on ground.…”

Section: General Reviewmentioning

confidence: 99%

“…The AWIFS versus LISS-III [21] data set have been used for the purpose of classification, whereby LISS-III versus LISS-IV data set has been used to generate reference data and AWIFS versus LISS-IV data is used for the purpose of similarity analysis (Table 1).…”

Section: Study Area and Data Usedmentioning

confidence: 99%

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A Review of Soft Classification Approaches on Satellite Image and Accuracy Assessment

Sharma

Goyal

Dwivedi

2016

Advances in Intelligent Systems and Computing

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Classification is a widely used technique for image processing and is used to extract thematic data for preparing maps in remote sensing applications. A number of factors affect the classification process. But classification is only half part of image processing and incomplete without accuracy assessment. Accuracy assessment of classification tells how accurately the classification process has been carried out. This research paper presents a review study of image classification through soft classifiers and also presents accuracy assessment of soft classifiers using entropy. Soft classifiers help in the development of more robust methods for remote sensing applications as compared to the hard classifiers. In this paper, two supervised soft classifiers, FCM, and PCM have been used to demonstrate the improvement in the classification accuracy by membership vector, RMSE, and also it has tried to generate fraction output from FCM, PCM, and noise with entropy.

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“…Chapter 3 discusses a filtering step built upon the use of some operators of mathematical morphology as part of an integrated adaptive spatial approach that can be used to improve urban mapping from very-high-resolution remote sensor data. Finally, due to the space limit, we are not able to cover some other pattern classification techniques that can be used to improve urban mapping, such as expert systems (e.g.., Stefanov, Ramsey and Christensen, 2001), support vector machines (e.g., Yang, 2011), or a fuzzy classifier (e.g., Shalan, Arora and Ghosh, 2003). Readers who are interested in learning more about these methods should refer to the references provided.…”

Section: Algorithms and Techniques For Urban Attribute Extractionmentioning

confidence: 99%

What is Urban Remote Sensing?

Yang

2011

Urban Remote Sensing

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This introductory chapter defines the scope of urban remote sensing research. It begins with a discussion on the rationale leading to the development of remote sensing for urban studies and the motivation behind this book project emphasizing the need to adopt a broad vision on urban remote sensing research. It then discusses the benefits and possible challenges of using remote sensing for urban studies, followed by an overview of the major topics discussed in the book. Finally, the chapter highlights several areas that need further attention.

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An evaluation of fuzzy classifications from IRS 1C LISS III imagery: A case study

Cited by 35 publications

References 0 publications

Textural and knowledge-based lithological classification of remote sensing data in Southwestern Prieska sub-basin, Transvaal Supergroup, South Africa

Textural and knowledge-based lithological classification of remote sensing data in Southwestern Prieska sub-basin, Transvaal Supergroup, South Africa

A Review of Soft Classification Approaches on Satellite Image and Accuracy Assessment

What is Urban Remote Sensing?

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