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Multimedia Image Data Analysis Based on KNN Algorithm.

Runya Li¹, Shenglian Li²

¹Research Institute of Finance, Hebei Finance University, Baoding, Hebei 071051, China.

Computational Intelligence and Neuroscience

|April 22, 2022

Summary

This summary is machine-generated.

The study enhanced multispectral remote sensing data analysis by combining KNN and hyperspectral technology. Support Vector Machine (SVM) classification achieved the highest accuracy, proving effective for CHRIS multiangle hyperspectral data.

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Area of Science:

Remote Sensing
Data Analysis
Multimedia Technology

Background:

Improving the authenticity of multispectral remote sensing image data analysis is crucial.
Advanced multimedia and spectral technologies can be combined to subdivide the spectrum.

Purpose of the Study:

To evaluate different classification methods for CHRIS (Compact High Resolution Imaging Spectrometer) data.
To compare the classification accuracy of single-angle, multiangle combined, and band-combined CHRIS images.
To determine the optimal classification method for CHRIS multiangle hyperspectral data.

Main Methods:

Utilized the KNN algorithm and hyperspectral remote sensing technology.
Applied various classification methods, including Support Vector Machine (SVM), to CHRIS 0° data.
Classified CHRIS images from five different viewing angles (FZA) and analyzed multiangle combined and band-combined images.

Main Results:

SVM classification achieved the highest accuracy (72.8448%) with a Kappa coefficient of 0.6770 for CHRIS 0° data.
Classification accuracy varied with viewing angles, with FZA=0° yielding the highest accuracy.
The overall classification accuracy of angle-combined images was lower than single-angle images, and band-combined images showed very low accuracy for forest types.

Conclusions:

The Support Vector Machine (SVM) method is the most effective for classifying CHRIS multiangle hyperspectral remote sensing image data.
Optimal classification is achieved using single-angle CHRIS data, particularly at FZA=0°.
Combining multiangle or spectral bands did not improve classification accuracy compared to optimal single-angle classification.