Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model
Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model
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DOI：
10.1109/access.2020.2986267
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Cai, Weiwei
中科院分区：
文献类型：
--
作者：
Wang, Zesong;Zou, Cui;Cai, Weiwei
虽然高光谱遥感图像具有丰富的光谱特征，但对于小样本的遥感图像，特征选择、特征挖掘和特征融合是非常重要的。单一模型难以适用于训练过程中的特征选择、特征挖掘、特征整合等多项任务，导致高光谱图像小样本分类效果不佳。为了提高小样本分类能力，提出了一种序贯联合深度学习算法。(In该算法通过使用双向长短期记忆（Bi-LSTM）和AML，集成了注意力机制下多尺度卷积的深层特征。首先，我们使用主成分分析（PCA）来降低高光谱数据的维数，并保留其关键特征。其次，该模型使用一个集成的注意力机制来分配的关键输入功能的概率权重。第三，该模型使用多尺度卷积挖掘特征后的分布权重，以获得深层特征。第四，该模型使用双向长短期记忆（Bi-LSTM）来整合不同尺度的卷积结果。最后，使用softmax分类器完成多类高光谱遥感图像的分类。在三个公开的高光谱数据集上进行了实验，结果证明了该算法的有效性，从而在小样本高光谱图像（HSI）预测中表现出了强大的性能。
Although hyperspectral remote sensing images have rich spectral features, for small samples of remote sensing images, feature selection, feature mining, and feature integration are very important. A single model is difficult to apply to multiple tasks such as feature selection, feature mining, and feature integration during training, resulting in poor classification results for small sample classification of hyperspectral images. To improve the classification of small samples, a sequential joint deep learning algorithm is proposed in this paper. (In this algorithm, the deep features of multiscale convolution under an attention mechanism are integrated by using Bidirectional Long Short-Term Memory(Bi-LSTM) and AML.) First, we used principal component analysis (PCA) to reduce the dimensionality of the hyperspectral data and retain their key features. Second, the model uses an integrated attention mechanism to distribute the probability weight of the key input feature. Third, the model uses multiscale convolution to mine features after the distribution weight to obtain deep features. Fourth, the model uses bidirectional long short-term memory (Bi-LSTM) to integrate the convolution results at different scales. Finally, the softmax classifier is used to complete the classification of multiclass hyperspectral remote sensing images. Experiments were carried out on three public hyperspectral data sets, and the results proved that our proposed AML algorithm is effective, thus demonstrating powerful performance in the prediction of hyperspectral images (HSIs) of small samples.