Clustering Based Band Selection: Classification of Hyperspectral Images

Kalidindi, Kishore Raju; G. P., Saradhi Varma; D., Rajyalakshmi

ISBN 10: 6204957244 / ISBN 13: 9786204957241

Verlag: LAP LAMBERT Academic Publishing, 2022

Beschreibung

Print on Demand.

Bestandsnummer des Verkäufers 400100775

Bibliografische Details

• Titel: Clustering Based Band Selection: Classification of Hyperspectral Images
• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 6204957244
• ISBN-13: 9786204957241
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Hyperspectral Images (HSIs) are popular in diversified applications, such as; Geo-sciences, Biomedical imaging, Agriculture, and physics-related research. The rich spatial and spectral information of HSI are the key factors for robust representation of class-specific objects, in remote sensing applications. But these images often suffer from the Hughes effect. This demands a dimensionality reduction using feature selection. The feature selection process is commonly called Band Selection (BS) for the HS dataset. This Book is mainly focused on three proposed models, where, mostly the clustering based unsupervised strategies are adopted for BS. First, Derivative-based band clustering and multi-agent PSO optimization for optimal band selection (DBC_MAPSO) is proposed. But they are time consuming and the selected bands are not persistent for each evaluation, due to the random nature of the optimizers. To overcome this, Spatial residual clustering and entropy-based ranking (SRC_EBR) and Featured clustering and ranking based bad cluster removal (FC_RBCR) are proposed.