A Spectral-Spatial Change Detection Method Based on Simplified 3-D Convolutional Autoencoder for Multitemporal Hyperspectral Images

Change detection for multitemporal hyperspectral images (HSIs) has always been a research hotspot of remote sensing. However, most current detection methods only use spectral information or spatial information separately, and there are many false detection areas in the detection results. Besides, th...

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Vydané v:	IEEE geoscience and remote sensing letters Ročník 19; s. 1 - 5
Hlavní autori:	Zhao, Chunhui, Cheng, Hao, Feng, Shou
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	3-D convolutional autoencoder (3DCAE) Change detection Classifiers Convolution Data analysis Data mining Data processing Decoding Detection Feature extraction hyperspectral images (HSIs) Hyperspectral imaging Kernel Methods Neural networks Redundancy Remote sensing Spatial data spatial–spectral information Spectra Training
ISSN:	1545-598X, 1558-0571
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