Spectral-Spatial Classification with Naive Bayes and Adaptive FFT for Improved Classification Accuracy of Hyperspectral Images

This paper presents a post-processing-based Spectral-Spatial Classification (SSC) approach for Hyperspectral (HS) images. The approach effectively overcomes the limitations of traditional pixel-based classifiers by integrating spectral and spatial information to achieve improved classification resul...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 17; S. 1 - 14
Hauptverfasser:	Singh, Arvind Kumar, Sunkara, Renuvenkataswamy, Kadambi, Govind R., Palade, Vasile
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptive Fast Fourier Transform Adaptive fast Fourier transform (AFFT) Algorithms Classification Classification algorithms Classifiers Fast Fourier transformations Feature extraction Fourier transforms hyperspectral (HS) image Hyperspectral Image Spectral-Spatial Classification Hyperspectral imaging Image classification Iteration-wise Variable Sequencing (IVS) iterationwise variable sequencing (IVS) Iterative methods Machine learning Naive Bayes Naive Bayes (NB) Principal component analysis Principal components analysis Spatial data spectral–spatial classification (SSC) Support vector machines Training Training data
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
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