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首页> 外文期刊>Journal of visual communication & image representation >Classification of farmland images based on color features
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Classification of farmland images based on color features

机译：基于颜色特征的农田图像分类

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Farmland images recognition and classification are of great significance in farmland environmental perception. Since the open and unstructured farmland environment has complex scenes, and is easily affected by various factors, furthermore, environmental information is uncertain and hard to predict. Based on hue saturation value (HSV), hue saturation lightness (HSL) and hue saturation intensity (HSI) color space models, taking use of image analysis and classification technology, this paper realizes the classification of farmland images in different environments. On the basis of color space, eight color features of the images are extracted. First, we conducted non equal interval quantification and drew the color feature curves, after that, we selected five eigenvectors which can correctly classify the images. Then, principal component analysis (PCA) was used for dimension reduction. Finally, radial basis function (RBF) neural network was joined for the extraction of images in the same scenes and different ones. The performance of the use of multiple color spaces combining with PCA and RBF shows that the average recognition rates of sunny days and cloudy days in the same scenes and different scenes are 100%, 87.36% and 84.58%, 68.11% respectively. Therefore, this method has higher recognition rate than BP neural network. (C) 2015 Elsevier Inc. All rights reserved.
机译：农田图像的识别和分类对农田环境感知具有重要意义。由于开放，非结构化的农田环境场景复杂，容易受到各种因素的影响，因此环境信息的不确定性和预测性也很差。基于色相饱和度值（HSV），色相饱和度明度（HSL）和色相饱和度强度（HSI）色彩空间模型，利用图像分析和分类技术，实现了不同环境下农田图像的分类。基于色彩空间，提取图像的八个色彩特征。首先，我们进行了非等间隔量化并绘制了颜色特征曲线，然后，我们选择了五个可以正确分类图像的特征向量。然后，使用主成分分析（PCA）进行尺寸缩减。最后，加入了径向基函数（RBF）神经网络，用于提取相同场景和不同场景中的图像。将多种色彩空间与PCA和RBF结合使用的性能表明，同一场景和不同场景中晴天和阴天的平均识别率分别为100％，87.36％和84.58％，68.11％。因此，该方法具有比BP神经网络更高的识别率。 （C）2015 Elsevier Inc.保留所有权利。

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