Abstract
Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.
Highlights
Sea ice is one of the most prominent marine disasters in the polar and mid- and high-latitude regions [1]
The rich and representative samples are chosen by combining the idea of active learning and input into support vector machines (SVMs) classifier to achieve high classification performance of sea ice remote sensing image in the case of small number of samples. e contributions of this paper are as follows: (1) this paper proposes a new method SE-convolutional neural network (CNN)-SVM for remote sensing sea ice image classification. is method designs and constructs the 3DCNN model, which can simultaneously extract the spatial information and spectral information of sea ice images and fully exploit the spatial-spectrum characteristics of sea ice hidden in remote sensing data
Due to the advantages of SVM classifiers in dealing with small samples and nonlinear high-dimensional feature classification problems, compared with CNN methods, CNN-SVM can obtain better classification results than CNN’s own softmax classifier. e method proposed in this paper considers the small sample problem and the complex correlation among spectra, 3D-CNN is used to extract different types of sea ice features, and SE-Block is integrated to optimize the weight of each spectral feature, further distinguishing the contribution of different spectral features to sea ice classification
Summary
Sea ice is one of the most prominent marine disasters in the polar and mid- and high-latitude regions [1]. (2) Due to the high correlation among multiple spectral channels in remote sensing sea ice data, and the different channels have different degrees of discrimination for sea ice classification, the proposed method combines 3D-CNN network with the SE-Block to distinguish the different contributions of different spectral features and weight the spectral channels in order to improve the sample quality further in sea ice classification. (3) Because SVM has obvious advantages in solving small samples and high dimensional nonlinear problems, the proposed method extracts the spatial spectral feature and weight based on the 3D-CNN fusing SE-Block, combining the active learning method to choose the rich and representative samples and input into SVM classifier for classification, which achieves superior sea ice classification performance in the case of small number of samples.
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