Hyperspectral Image Classification Using Spectral-Spatial LSTMs
Hyperspectral Image Classification Using Spectral-Spatial LSTMs
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DOI：
10.1007/978-981-10-7299-4_48
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
F. Zhou;Renlong Hang;Qingshan Liu;Xiaotong Yuan
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文献类型：
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作者：
F. Zhou;Renlong Hang;Qingshan Liu;Xiaotong Yuan
本文提出了一种基于光谱空间长短期记忆（LSTM）网络的高光谱图像分类方法。具体来说，对于每个像素点，我们将其不同通道的光谱值逐一输入到光谱LSTM中，学习光谱特征。同时，我们首先使用主成分分析（PCA）从HSI中提取第一主成分，然后从中选择以每个像素为中心的局部图像补丁。然后，我们将每个图像patch的行向量依次输入到Spatial LSTM中，学习中心像素的空间特征。在分类阶段，将每个像素点的光谱特征和空间特征分别输入到softmax分类器中，得到两个不同的分类结果，并进一步采用决策融合策略得到一个联合的光谱-空间结果。在Indian Pines、Pavia University和Kennedy Space Center这三个被广泛使用的HSIs上的实验结果表明，与其他最先进的方法相比，我们的方法的分类准确率至少提高了2.69%、1.53%和1.08%。
In this paper, we propose a hyperspectral image (HSI) classification method using spectral-spatial long short term memory (LSTM) networks. Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature. Meanwhile, we firstly use principle component analysis (PCA) to extract the first principle component from a HSI, and then select local image patches centered at each pixel from it. After that, we feed the row vectors of each image patch into Spatial LSTM one by one to learn the spatial feature for the center pixel. In the classification stage, the spectral and spatial features of each pixel are fed into softmax classifiers respectively to derive two different results, and a decision fusion strategy is further used to obtain a joint spectral-spatial results. Experimental results on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center) show that our method can improve the classification accuracy by at least 2.69%, 1.53% and 1.08% compared to other state-of-the-art methods.