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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Satellite Image Classification by Narrow Band Gabor Filters and Artificial Neural Networks
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Satellite Image Classification by Narrow Band Gabor Filters and Artificial Neural Networks

机译：窄带Gabor滤波器和人工神经网络对卫星图像进行分类

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摘要

Satellite image segmentation is an important task to generate classification maps. Land areas are classified and clustered into groups of similar land cover or land use by segmentation of satellite images. It may be broad classification such as urban, forested, open fields and water or may be more specific such as differentiating corn, soybean, beet and wheat fields. One of the most important among them is partitioning the urban area to different regions. On the other hand Multi-Channel filtering is used widely for texture segmentation by many researchers. This paper describes a texture segmentation algorithm to segment satellite images using Gabor filter bank and neural networks. In the proposed method feature vectors are extracted by multi-channel decomposition. The spatial/spatial-frequency features of the input satellite image are extracted by optimized Gabor filter bank. Some important considerations about filter parameters, filter bank coverage in frequency domain and the reduction of feature dimensions are discussed. A competitive network is trained to extract the best features and to reduce the feature dimension. Eventually a Multi-Layer Perceptron (MLP) is employed to accomplish the segmentation task. Our MLP uses the sigmoid transfer function in all layers and during the training, random selected feature vectors are assigned to proper classes. After MLP is trained the optimized extracted features are classified into sections according to the textured land cover regions.
机译：卫星图像分割是生成分类图的重要任务。通过分割卫星图像，将土地区域分类并归类为相似的土地覆盖或土地用途。它可能是广义的分类，例如城市，森林，空地和水，或者可能更具体，例如区分玉米，大豆，甜菜和麦田。其中最重要的一项就是将市区划分为不同的区域。另一方面，许多研究人员将多通道过滤广泛用于纹理分割。本文描述了一种纹理分割算法，利用Gabor滤波器组和神经网络对卫星图像进行分割。在提出的方法中，特征向量是通过多通道分解提取的。通过优化的Gabor滤波器组提取输入卫星图像的空间/空间频率特征。讨论了有关滤波器参数，频域中滤波器组覆盖率和特征尺寸减小的一些重要注意事项。竞争网络经过培训，可以提取最佳特征并减小特征尺寸。最终，采用多层感知器（MLP）来完成分割任务。我们的MLP在所有层中都使用S型传递函数，并且在训练过程中，随机选择的特征向量将分配给适当的类。训练MLP后，根据纹理化的土地覆盖区域将优化的提取特征分类为多个部分。

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