Advanced image processing algorithms assist operators with change detection, image classification, identifying anomalies and tracking patterns of activity over time.
先进的图像处理算法协助操作人员随时进行变化检测、图像分类、识别异常和跟踪活动模式。
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。
Synthetic Aperture Radar (SAR) image classification is a key technique of SAR image interpretation.
合成孔径雷达(SAR)图像分类是SAR图像解译的关键技术之一。
The topic on how to bridge the semantic gap effectively and improve the accuracy of image classification has been widely explored.
如何缩小语义鸿沟，提高图像分类系统的性能，是一个得到广泛研究的课题。
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。
RS image classification and information derivation of mosquito habitats.
蚊媒孳生环境遥感分类和遥感信息提取研究。
The experimental result shows that this model has high accuracy in image classification.
实验结果表明：该模型在图像分类中有较高正确率。
Then support vector machines using the kernel are trained to perform image classification.
然后将该核函数应用于支持向量机，对图像进行分类。
The post-processing of image classification is an important procedure in classified mapping.
图像分类后处理是图像分类制图过程中十分重要的一个步骤。
Unfortunately, in hyper-spectral image classification, training samples are usually limited.
然而在高光谱图像分类中，训练样本通常是有限的。
In this paper a novel approach for image classification based on feature element is proposed.
本文提出了一种基于特征元素的图象分类方法。
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。
In remote sensing image processing, image classification plays the role of feature's extraction.
在遥感图像的处理中，图像分类起到特征提取的作用。
Fast and accurate SAR image classification is a very pivotal part in the practical applications.
快速、准确的SAR图像分类是实现各种实际应用的前提。
In this thesis, SVM as a new machine learning method is brought into medical image classification.
论文将支持向量机的机器学习方法引入到医学图像的分类问题。
Medical image classification plays a central role in automation of information system of hospitals.
医学图像的自动分类对于医院的信息自动化有重要的意义。
The model proved to be able to increase the accuracy of crop identification and image classification.
通过分类验证，使用该模型进行图像增强后，有助于提高农作物识别与图像分类的精度。
Data mining based medical image classification methods is an important component of multimedia data mining.
基于数据挖掘的医学图像分类方法研究是多媒体数据挖掘的一个重要组成部分。
This paper firstly investigates the application of support vector machines for medical image classification.
本文首次采用支持向量机方法对医学图像进行了分类研究。
The area data of image classification was fairly close to those of second class investigation in each town.
各镇（林场）地类、林分面积与二类调查数据比较接近。
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。
This paper applies UML to design an industrial CT image classification system, and implements it on the VC6.0platform.
论文介绍了应用uml对工业CT扫描图像自动分级系统进行建模，并在VC 6.0平台上实现该系统的过程。
Appropriate texture features are selected with this method to serve image classification and simulative results are discussed.
使用这一方法挑选合适的纹理特征用于图像分类，并对结果进行分析。
The paper designed a new histogram kernel function for support vector machines which achieved good results in image classification .
说明：为支持向量机设计了一种新的直方图核函数运用在图像分类上取得不错的效果。
The proposed approach can generalize well on difficult image classification problems, especially, for images taken in different views.
本文提出的算法能够在较困难的图像分类问题上得到较好的结果，尤其是为不同角度拍摄的物体影像分类。
The test shows that multi-scale Lempel-Ziv complexity can be used as an image classification method or as a feature of image classifier.
该结果说明：不同尺度下复杂性的差异可以作为图像分类的新方法或者作为现有分类器的特征。
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。
This paper proposes a fast algorithm for texture feature extraction. The new algorithm is suitable for remote image classification on line.
给出一种抽取纹理特征的算法，该算法实时性强，适于在线遥感图像分类。
Selection of optimal feature combinations is one of the key procedures for remotely sensed image classification and information abstraction.
最佳特征影像组合的选择是决定遥感影像信息提取与影像分类效果的关键环节之一。
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。
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