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%.