Feature selection and image classification using rough sets theory

Pessoa, Alex Sandro Aguiar; Stephany, Stephan; Fonseca, Leila María García

doi:10.1109/igarss.2011.6049822

2011 IEEE International Geoscience and Remote Sensing Symposium 2011

DOI: 10.1109/igarss.2011.6049822

|View full text |Cite

Feature selection and image classification using rough sets theory

Alex Sandro Aguiar Pessoa

Stephan Stephany

Leila María García Fonseca

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

2020

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [10], Peters et al used rough sets and nearness-like structures to describe and compare visual objects. Pessoa et al [7] used the concept of rough sets theory for image classification. In this paper, we use semi-uniform spaces in the framework of rough sets to study nearness of digital images.…”

mentioning

confidence: 99%

Rough semi-uniform spaces and its image proximities

Tiwari

Singh

2020

era

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

Rough semi-uniform spaces and its image proximities

Tiwari

Singh

2020

era

View full text Add to dashboard Cite

show abstract

Supervised band selection in hyperspectral images using single-layer neural networks

Habermann

Frémont

Shiguemori

2018

International Journal of Remote Sensing

View full text Add to dashboard Cite

Hyperspectral images provide fine details of the scene under analysis in terms of spectral information. This is due to the presence of contiguous bands that make possible to distinguish different objects even when they have similar colour and shape. However, neighbouring bands are highly correlated, and, besides, the high dimensionality of hyperspectral images brings a heavy burden on processing and also may cause the Hughes phenomenon. It is therefore advisable to make a band selection pre-processing prior to the classification task. Thus, this paper proposes a new supervised filter-based approach for band selection based on neural networks. For each class of the data set, a binary single-layer neural network classifier performs a classification between that class and the remainder of the data. After that, the bands related to the biggest and smallest weights are selected, so, the band selection process is class-oriented. This process iterates until the previously defined number of bands is achieved. A comparison with three state-of-the-art band selection approaches shows that the proposed method yields the best results in 43.33% of the cases even with greatly reduced training data size, whereas the competitors have achieved between 13.33% and 23.33% on the Botswana, KSC and Indian Pines datasets.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Feature selection and image classification using rough sets theory

Cited by 2 publications

References 4 publications

Rough semi-uniform spaces and its image proximities

Rough semi-uniform spaces and its image proximities

Supervised band selection in hyperspectral images using single-layer neural networks

Contact Info

Product

Resources

About