Based on the dimension reduction of hyperspectral remote sensing image, a new neural network method is used to classify the hyperspectral remote sensing image of carbon dioxide in detail. Firstly, the Kernel Principal Component Analysis (KPCA) and Genetic Algorithms (GA) are used to reduce the dimension of hyperspectral remote sensing images; secondly, the traditional remote sensing image classification methods (ISODATA, SVM), traditional neural networks (BP), and new neural networks are used to classify the hyperspectral remote sensing images. Finally, noise assessment method based on local mean and local standard deviation (LMLSD) of spectral image is used to evaluate the classification accuracy. In addition, hyperspectral remote sensing images are dimensionality reduction. Secondly, the comparison between the traditional remote sensing image classification method and the new neural network method is analyzed. Finally, a new neural network method is applied to classify hyperspectral remote sensing images.

基于高光谱遥感图像的降维，采用一种新的神经网络方法对二氧化碳的高光谱遥感图像进行了详细分类。首先，利用核主成分分析（KPCA）和遗传算法（GA）来减小高光谱遥感图像的尺寸。其次，利用传统的遥感图像分类方法（ISODATA，SVM），传统的神经网络（BP）和新的神经网络对高光谱遥感图像进行分类。最后，基于光谱图像的局部均值和局部标准差（LMLSD）的噪声评估方法被用于评估分类精度。另外，高光谱遥感图像是降维的。其次，分析了传统遥感图像分类方法与新型神经网络方法的比较。最后，采用一种新的神经网络方法对高光谱遥感图像进行分类。