The texture classification is completed with back propagation (BP) neural network.
最后利用反向传播(BP)神经网络进行纹理的分类识别。
This paper presents a texture classification approach based on function link network.
提出了一种基于函数联接的感知器神经网络的纹理分类方法。
This paper puts forward a new method of invariant texture classification for remote sensing image.
本文提出了一种遥感图像旋转不变纹理分类的新方法。
Based on frequency domain distribution and scale feature of textural information, we can do texture classification effectively.
该算法利用纹理信息的频域分布以及尺度特性，并在此基础上进行纹理分类。
In order to detect ship targets under complex sea background, the high-order fractal feature and its application to texture classification are analyzed.
将高阶分形特征用于海面运动目标检测，提取出用于区分运动目标和海杂波的新的分形特征——缝隙特征。
This paper deals with markov random field and it's application in image texture classification.
针对马尔可夫随机场方法用于影像纹理分类进行了探讨。
This method is compared with other rotation invariant texture classification algorithm, the experiment results show that it can improve the classification rate effectively.
将该方法与其它旋转不变纹理分类算法进行比较，实验结果表明，提出的算法能有效地提高正确分类率。
This paper discusses the shortage of conventional algorithms of texture classification based on wavelet transform, presents two improved approaches of point feature weighting and smart windows.
在基于小波的纹理分类算法的基础上，提出了逐点特征加权和活动窗口算法，使小波纹理分析能够用于高分辨率遥感影像的分类。
A method using two wavelet transform to get the texture feature for remote sensing is put forward, in order to turn out the invariant texture classification for remote sensing in the wavelet domain.
分析了有限脊小波变换可以实现图像的旋转不变性和平移不变性，提出了结合两种小波变换提取图像纹理特征的方法，实现了在小波域中进行图像的不变纹理分类。
A method using two wavelet transform to get the texture feature for remote sensing is put forward, in order to turn out the invariant texture classification for remote sensing in the wavelet domain.
分析了有限脊小波变换可以实现图像的旋转不变性和平移不变性，提出了结合两种小波变换提取图像纹理特征的方法，实现了在小波域中进行图像的不变纹理分类。
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