一种具有隐蔽色特征物体的图像分类方法研究

针对图像中某几类物体具有相似颜色特征而导致的分类困难问题,本文提出了一种具有隐蔽色特征物体的图像分类方法.该方法针对可见光图像中具有颜色隐蔽性物体而难以区分的问题,通过将二维图像的邻域像素空间特征与高光谱图像的谱段特征相结合并使用改进的局部线性嵌入降维算法实现了空谱联合的特征降维,最终利用主动学习胶囊网络训练高光谱数据...

A Method of Image Classification for Objects with Camouflaged Color Features

To solve the classification difficulty caused by different objects with similar color features in an image,this paper proposes an image classification method for objects with camouflaged color features.To alleviate the difficulty of distinguishing color-camouflaged objects in RGB images,this method not only combines the spatial domain features of the neighborhood pixels and the spectral domain features of the hyperspectral data,which realizes the spatial-spectrum joint feature construction,but also uses the improved LLE(local linear embedding)algorithm to accomplish spectral dimensional reduction.The proposed method uses an active learning capsule network to train a hyperspectral data classifier and classifies objects in the scene.Active learning can label more representative samples through the improved active learning function and realized capsule network training based on a minor sample dataset,which reduces the cost of sample labeling and model training significantly,thereby improving the classification performance of the model.Experiments show that the algorithm proposed in this paper can effectively classify camouflaged targets and other natural targets based on our self-made hyperspectral dataset.The average accuracy of camouflaged targets was 91%,and the average accuracy of all target types was 89.9%.