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Open AccessArticle

Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification

by Simin Li 1, Xueyu Zhu 2 and Jie Bao 1,*
1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1714; https://doi.org/10.3390/s19071714
Received: 2 March 2019 / Revised: 5 April 2019 / Accepted: 7 April 2019 / Published: 10 April 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of various shapes and sizes of objects over the image plane, leading to missing scale-dependent information. In this paper, we propose a hierarchical multi-scale convolutional neural networks (CNNs) with auxiliary classifiers (HMCNN-AC) to learn hierarchical multi-scale spectral–spatial features for HSI classification. First, to better exploit the spatial information, multi-scale image patches for each pixel are generated at different spatial scales. These multi-scale patches are all centered at the same central spectrum but with shrunken spatial scales. Then, we apply multi-scale CNNs to extract spectral–spatial features from each scale patch. The obtained multi-scale convolutional features are considered as structured sequential data with spectral–spatial dependency, and a bidirectional LSTM is proposed to capture the correlation and extract a hierarchical representation for each pixel. To better train the whole network, weighted auxiliary classifiers are employed for the multi-scale CNNs and optimized together with the main loss function. Experimental results on three public HSI datasets demonstrate the superiority of our proposed framework over some state-of-the-art methods. View Full-Text
Keywords: hyperspectral image (HSI) classification; convolutional neural networks (CNNs); bidirectional LSTM; multi-scale features hyperspectral image (HSI) classification; convolutional neural networks (CNNs); bidirectional LSTM; multi-scale features
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Li, S.; Zhu, X.; Bao, J. Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification. Sensors 2019, 19, 1714.

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