Proceedings ArticleDOI
Cube-CNN-SVM: A Novel Hyperspectral Image Classification Method
Jiabing Leng,Tao Li,Gang Bai,Qiankun Dong,Han Dong +4 more
- pp 1027-1034
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TLDR
Experimental results indicate that the hyperspectral image classification can be improved efficiently with the spectral-spatial fusion strategy and CCS method.Abstract:
CNNs (convolutional neural networks) have been proved to be efficient deep learning models that can directly extract high level features from raw data. In this paper, a novel CCS (Cube-CNN-SVM) method is proposed for hyperspectral image classification, which is a spectral-spatial feature based hybrid model of CNN and SVM (support vector machine). Different from most of traditional methods that only take spectral information into consideration, a target pixel and the spectral information of its neighbors are organized into a spectral-spatial multi-feature cube used in hyperspectral image classification. It is a straightforward but valid spatial strategy that can easily improve classification accuracy without extra modification of deep CNN's structure except the size of input layer and convolutional kernel. Our deep CNN consists of the input layer, convolutional layer, max pooling layer, full connection layer and output layer. To further improve hyperspectral image classification accuracy, SVM is trained as hyperspectral image classifier with the features extracted by deep CNN from spectral-spatial fusion information. Three hyperspectral image datasets such as the KSC (Kennedy Space Center), PU (Pavia University Scene) and Indian Pines are used to evaluate the performance of CCS method. Experimental results indicate that the hyperspectral image classification can be improved efficiently with the spectral-spatial fusion strategy and CCS method. Firstly, it is easy to implement the spatial strategy to improve classification accuracy about 4% compared with only spectral information used for classification, in which 98.49% is gained on the KSC dataset. Secondly, CCS method can further improve classification accuracy about 1%~3% compared to the best performance of deep CNN, in which 99.45% is gained on the PU dataset.read more
Citations
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Journal ArticleDOI
Deep learning classifiers for hyperspectral imaging: A review
TL;DR: A comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature is provided, providing an exhaustive comparison of the discussed techniques.
Journal ArticleDOI
A survey: Deep learning for hyperspectral image classification with few labeled samples
TL;DR: Although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model.
Journal ArticleDOI
Flood susceptibility mapping using convolutional neural network frameworks
TL;DR: The most popular convolutional neural network (CNN) is introduced to assess flood susceptibility in Shangyou County, China and three data presentation methods are designed in the CNN architecture to fit the two proposed frameworks.
Proceedings ArticleDOI
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image
TL;DR: A novel convolutional neural network framework for the characteristics of hyperspectral image data called HSI-CNN, which can also provides ideas for the processing of one-dimensional data.
Journal ArticleDOI
Advances of Four Machine Learning Methods for Spatial Data Handling: a Review
Peijun Du,Peijun Du,Xuyu Bai,Xuyu Bai,Kun Tan,Zhaohui Xue,Alim Samat,Junshi Xia,Erzhu Li,Hongjun Su,Wei Liu +10 more
TL;DR: It can be concluded that machine learning methods are very effective for spatial data handling and have wide application potential in the big data era.
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