Hyperspectral Image Classification Based On Adaptive Neighborhood And Semi-supervised Active Learning

Hyperspectral image classification is an important technology in hyperspectral image processing.The space spectrum information which belongs to different objects in hyperspectral images is analyzed by computer and other equipment,and all pixels are divided into independent and non overlapping regions by effective classification,but the number of the available labeled samples is few,because the specific instruments and a lot of manpower and material resources are needed to label the samples.A series of research methods are proposed to solve this problem,such as semi-supervised learning,active learning,and generating the virtual samples by many specific methods.But most of these research methods,in the traditional way,are using the global feature space and the standard rectangle neighborhood.They often neglect the specific spatial information and spectral information of the hyperspectral images,and result in the lack of the specific solutions.Based on the above analysis,in this paper,three hyperspectral image classification methods based on adaptive neighborhood are proposed.The spatial and spectral information of the hyperspectral image are used by the maximum possible to improve the classification accuracy of hyperspectral image.The main contents are as follows:Firstly,a hyperspectral image classification model based on adaptive neighborhood and active learning is proposed.The labeled sample in hyperspectral image are limited.so,selecting training set with the most effective samples can improve the classification accuracy.The purpose of this model is to change the situation in the traditional active learning on how to select the most effective samples,which are often based on the decision surface of SVM in the feature space,and the method often results in uneven spatial distribution of the selected samples and the process of selecting samples is slow.In the model proposed in Section 3,which selecting the points farthest from the support vector in the adaptive neighborhood to marker the true class label.The adaptive neighborhood representates the similarity of characteristic and the support vector external representates contain more information,which are constrained at the same time.Selecting samples from the adaptive neighborhood with more information for the training set which can expanding the scale of the training set.A better classification result can be obtained by using these samples.And verify the model by the Indian Pines and Pavia University data sets.Secondly,a hyperspectral image classification model based on adaptive neighborhood and semi-supervised learning is proposed.This model aims to change the traditional semisupervised learning on utilizing the unlabeled samples from the global feature space or using space rectangular neighborhood.The classification accuracy caused by the error method of utilizing the unlabeled samples is not high,even if the accuracy is decline after a certain number of iterations.The model proposed in Section 4,which selecting the unlabeled samples in the adaptive neighborhood of the training sample with the same classification label as the label of training sample.By the same classification labels and both in the same adaptive neighborhood,the probability of the selected sample belongs to the same label as the training sample is greatly increased.Selecting such sample and adding it to the training set for training can greatly improve the classification results.And verify the model by the Indian Pines and Pavia University data sets.Thirdly,a hyperspectral image classification model based on semi-supervised learning and Stacked Auto-Encoder is proposed.The purpose of this model is to change the traditional linear combination in the feature space of linear separable with virtual sample generated by all positive weights that most of them are redundant samples.and a large number of virtual samples with false labels are generated under the condition that the feature space is nonlinear separable.In the model proposed in Section 5,choosing the samples in the adaptive neighborhood of training sample for combination when select the "Parent" samples.The choice of the combined weights are positive and negative together.Giving the same label as training samples and using the SVM classifier to remove redundant samples.Using such virtual samples generated to train the SAE for extracting feature which can increases the classification performance.And verify the model by the Indian Pines and Pavia University data sets.