Recently, hyperspectral image classification is attracting more and more attention. Since every pixel is represented by a high dimensional spectral vector, the ordinary machine learning algorithms usually require a large number of training samples to solve this problem. However, collecting labeled samples is time-consuming, which forces us to improve existing algorithms. Motivated by evolutional algorithms (EAs), we propose location based DNA matching algorithm for hyperspectral image classification. It aims mainly on the shortcomings such as requirement for large number of labeled samples and inseparable spectral values. It is based on EA and can be segmented into three subtasks. In the first encoding procedure, some spatial information is added to the spectral values to solve the problem of spectral mixture to some extent. In the second evolutional procedure, we introduce elite-preserving strategy and totally random operators within a specific exemplar, which can prevent deterioration and can also search for solutions in a large space. Aiming at the compared pixel-wise algorithms will end up with lots of mislabeled points in a region, we add the third procedure which utilizes some labeled samples’ locations to optimize the intermediate result. Compared with three state-of-the-art algorithms, simulation results suggest the effectiveness of the proposed algorithm.

中文翻译：

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一种基于位置的DNA高光谱图像分类新算法

近来，高光谱图像分类吸引了越来越多的关注。由于每个像素都由高维频谱矢量表示，因此普通的机器学习算法通常需要大量的训练样本来解决此问题。但是，收集标记的样本非常耗时，这迫使我们改进现有算法。受进化算法（EA）的启发，我们提出了基于位置的DNA匹配算法进行高光谱图像分类。它主要针对缺点，例如需要大量标记的样本和不可分离的光谱值。它基于EA，可以分为三个子任务。在第一编码过程中，将一些空间信息添加到频谱值以在某种程度上解决频谱混合的问题。在第二个进化过程中，我们在一个特定的样本中引入了保留精英的策略和完全随机的算子，这可以防止恶化并且还可以在较大的空间中寻找解决方案。针对比较的逐像素算法最终会在一个区域中出现许多标记错误的点，我们添加了第三种方法，该方法利用一些标记样本的位置来优化中间结果。与三种最新算法相比，仿真结果表明了该算法的有效性。我们添加了第三种程序，该程序利用一些标记样品的位置来优化中间结果。与三种最新算法相比，仿真结果表明了该算法的有效性。我们添加了第三种程序，该程序利用一些标记样品的位置来优化中间结果。与三种最新算法相比，仿真结果表明了该算法的有效性。