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Open AccessArticle

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

Marine Remote Sensing and Navigation Lab, College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
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Remote Sens. 2019, 11(18), 2170; https://doi.org/10.3390/rs11182170
Received: 1 August 2019 / Revised: 9 September 2019 / Accepted: 13 September 2019 / Published: 18 September 2019
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral-spatial-joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral-spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three-dimensional convolutional neural network (3D-CNN) model is designed to extract the deep spectral-spatial features of sea ice. The proposed method combines relevant textural features and performs spectral-spatial feature extraction based on the 3D-CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral-spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91%) with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images. View Full-Text
Keywords: sea ice; hyperspectral images (HSIs); gray-level co-occurrence matrix (GLCM); spectral-spatial-joint features; unlabeled samples; convolutional neural network (CNN) sea ice; hyperspectral images (HSIs); gray-level co-occurrence matrix (GLCM); spectral-spatial-joint features; unlabeled samples; convolutional neural network (CNN)
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MDPI and ACS Style

Han, Y.; Gao, Y.; Zhang, Y.; Wang, J.; Yang, S. Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning. Remote Sens. 2019, 11, 2170. https://doi.org/10.3390/rs11182170

AMA Style

Han Y, Gao Y, Zhang Y, Wang J, Yang S. Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning. Remote Sensing. 2019; 11(18):2170. https://doi.org/10.3390/rs11182170

Chicago/Turabian Style

Han, Yanling; Gao, Yi; Zhang, Yun; Wang, Jing; Yang, Shuhu. 2019. "Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning" Remote Sens. 11, no. 18: 2170. https://doi.org/10.3390/rs11182170

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