Convolutional neural network in network (CNNiN): hyperspectral image classification and dimensionality reduction. Issue 2 (1st February 2019)

• Record Type: Journal Article
• Authors: Shamsolmoali, Pourya; Zareapoor, Masoumeh; Yang, Jie
• Is Part Of: IET image processing. Volume 13:Issue 2(2019)
• Page Start: 246
• Page End: 253
• Publication Date: 2019-02-01
• Subjects: learning (artificial intelligence) -- hyperspectral imaging -- image classification -- geophysical image processing -- feature extraction -- recurrent neural nets; convolutional neural network in network -- CNNiN -- hyperspectral image classification -- dimensionality reduction -- deep learning approach -- HSI classification -- network pipeline -- spatial‐spectral relationship -- feature extraction -- recurrent neural network; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1375
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Classification is a principle technique in hyperspectral images (HSIs), where a label is assigned to each pixel based on its characteristics. However, due to lack of labelled training instances in HSIs and also its ultra‐high dimensionality, deep learning approaches need a special consideration for HSI classification. As one of the first works in the HSI classification, this study proposes a novel network pipeline called convolutional neural network in network (which is deeper than the existing approaches) by jointly utilising the spatial and spectral information and produces high‐level features from the original HSI. This can occur by using spatial–spectral relationships of individual pixel vector at the initial component of the proposed pipeline; the extracted features are then combined to form a joint spatial–spectral feature map. Finally, a recurrent neural network is trained on the extracted features which contain wealthy spectral and spatial properties of the HSI to predict the corresponding label of each vector. The model has been tested on two large scale hyperspectral datasets in terms of classification accuracy, training error, and computational time.