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首页> 外文会议>IFIP WG 12.5 International Conference on artificial intelligence applications and innovations >Artificial Neural Network Approach for Land Cover Classification of Fused Hyperspectral and Lidar Data
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Artificial Neural Network Approach for Land Cover Classification of Fused Hyperspectral and Lidar Data

机译：人工神经网络方法在高光谱和激光雷达融合数据土地覆盖分类中的应用

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摘要

Hyperspectral remote sensing images are consisted of several hundreds of contiguous spectral bands that can provide very rich information and has the potential to differentiate land cover classes with similar spectral characteristics. LIDAR data gives detailed height information and thus can be used complementary with Hyperspectral data. In this work, a hyperspectral image is combined with LIDAR data and used for land cover classification. A Principal Component Analysis (PCA) is applied on the Hyperspectral image to perform feature extraction and dimension reduction. The first 4 PCA components along with the LIDAR image were used as inputs to a supervised feedforward neural network. The neural network was trained in a small part of the dataset (less than 0.4%) and a validation set, using the Bayesian regularization backpropa-gation algorithm. The experimental results demonstrate efficiency of the method for hyperspectral and LIDAR land cover classification.
机译：高光谱遥感图像由数百个连续的光谱带组成，这些光谱带可以提供非常丰富的信息，并具有区分具有相似光谱特征的土地覆盖类别的潜力。 LIDAR数据可提供详细的高度信息，因此可与高光谱数据互补使用。在这项工作中，将高光谱图像与LIDAR数据相结合，并用于土地覆盖分类。将主成分分析（PCA）应用于高光谱图像，以执行特征提取和降维。前4个PCA组件与LIDAR图像一起用作监督前馈神经网络的输入。使用贝叶斯正则反向传播算法，在数据集的一小部分（少于0.4％）和验证集中训练了神经网络。实验结果证明了该方法对高光谱和LIDAR土地覆盖分类的有效性。

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