Improving classification accuracy is always the focus for remote sensing image classification.
提高遥感图像分类精度一直是受到普遍关注的焦点问题。
Texture analysis has become an important means for improving the accuracy of remote sensing image classification.
纹理分析是提高遥感影像分类精度的重要手段之一。
Current remote sensing image classification models and algorithms commonly used statistical methods, neural networks, Bayesian and so on.
目前遥感影像分类的常用模型和算法有统计学方法、神经网络、贝叶斯等。
In this paper, we propose an automatic multispectral remote sensing image classification technique based on improved probabilistic diffusion.
为了提高遥感图像分类精度，提出了一种基于概率扩散模型的多光谱遥感图像自动分类技术。
Then, an experiment of remote sensing image classification is carried out to verify the authenticity based on the relations between samples and bands.
然后，在遥感影像分类实验中，借助样本数量与波段数目的关系，验证了理论分析的结果。
Smooth implementation of this project, for solving uncertain information in remote sensing image classification has a high theoretical value and a good prospect.
本项目的顺利开展，对于解决遥感影像中不确定信息分类问题具有较高的理论价值和良好的应用前景。
The main drawback of traditional remote sensing image classification methods is its low precision. A neural network-based remote sensing image classification technique has been presented.
针对传统的遥感图像分类方法分类精度低的缺点，提出了一种基于神经网络的分类方法。
Remote sensing image classification is an important research field of information processing.
遥感影像分类是遥感信息处理的重要研究领域之一。
Remote sensing image classification is an important means for quantified remote sensing image analysis, and remote sensing image fusion can effectively improve the accuracy of image classification.
遥感影像分类是遥感定量化分析的重要手段，遥感影像融合是提高分类正确率的有效途径之一。
Remote sensing image classification is an important means for quantified remote sensing image analysis, and remote sensing image fusion can effectively improve the accuracy of image classification.
遥感影像分类是遥感定量化分析的重要手段，遥感影像融合是提高分类正确率的有效途径之一。
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