Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks

用RNN来做像素分类，输入是一系列相近的像素，长度人为指定为l，相近是利用像素相似度或是范围相似度得到的，计算个欧氏距离或是SAM。

数据是两个高光谱数据

1、Pavia University，Reflective Optics System Imaging Spectrometer (ROSIS) sensor得到，102个波段，1.3米空间分辨率，总大小610*340像素，9类地物

2、Salinas image，Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)，204个有效波段，3.7米空间分辨率，总大小512*217像素，16类地物

网络结构：

主要亮点在于像素点的选择吧，通过 Pixel Matching 和 Block Matching 来计算相似度距离，将距离按升序排列，选前n个距离最小的作为LSTM的输入，得到结果

参考文献：

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Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks

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