Multispectral Image Super Resolution with Auto-Encoder Model and Fusion Technique

Obtaining High Resolution(HR) Multispectral Images which are not readily available is one of the more critical objectives in remote sensing applications as these images can be used for various agricultural applications and previously various other methods like pansharpening have been introduced. Thi...

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Veröffentlicht in:2022 7th International Conference on Communication and Electronics Systems (ICCES) S. 1485 - 1490
Hauptverfasser: Reddy, Kasu Ameesh, Teja, Potti Sri Venkata Surya Pavan, Teja, Grandhi Krishna, Divya, Karanam, Aravinth, J
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2022
Schlagworte:
Analytical models
Convolutional Autoencoder
Discrete Wavelet Transform(DWT)
Discrete wavelet transforms
Measurement
Multispectral Images
Pansharpening
Principal Component Analysis(PCA)
Satellites
Stationary Wavelet Transform(SWT)
Training
Wavelet analysis
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Zusammenfassung:Obtaining High Resolution(HR) Multispectral Images which are not readily available is one of the more critical objectives in remote sensing applications as these images can be used for various agricultural applications and previously various other methods like pansharpening have been introduced. This paper proposes a novel convolutional auto-encoder for training the multispectral images obtained from Sentinel -2A Satellite and then pass the degraded multispectral image to obtain the reconstructed Multispectral Image which is spectrally enhanced and then fuse the image obtained from reconstruction with the original degraded image to obtain a spatial HR Multispectral Image. This fusion is done using various state of the art methods like Principal Component Analysis(PCA), Discrete Wavelet Transform Level-l(DWT) and Stationary Wavelet Transform Level-l(SWT) and the performance metrics.
DOI:10.1109/ICCES54183.2022.9835943