基于卷积神经网络与主动学习的高光谱图像分类

在利用主动学习方法进行高光谱图像分类时，往往存在空-谱特征不能得到有效利用和样本需要进行手动标注的问题。针对这些问题，提出一种结合卷积神经网络的主动学习方法进行高光谱图像分类。该方法首先提取像素的空间邻域组成训练样本，通过卷积神经网络对样本的空间特征和光谱特征进行学习并对数据进行初步分类；然后，基于高光谱图像的空间相似性和光谱相似性，对无标注样本进行标注，并将其加入标注训练集以提高分类器的分类精度。在Salinas、PaviaU和Indian Pines这3个高光谱数据上的实验结果表明，该方法能在较少标注样本的情况下，有效提高高光谱图像的分类精度。

Hyperspectral image classification based on convolutional neural network and active learning algorithm

When we apply the active learning method to the hyperspectral image classification, there always exist problems that the spectral-spatial features cannot be effectively utilized and the samples need to be manually labeled. In this work, we propose an active learning method combined with the convolutional neural networks to solve the above problems. Firstly, the neighborhood pixels of each pixel are extracted to form training samples. Then the spatial and spectral features of the samples are trained by the convolutional neural network and the data are preliminarily classified. Then, based on the spatial similarity and spectral similarity of the hyperspectral image, some of the unlabeled samples are labeled and added to the training set, which will further increase the accuracy of the classifier. Validated by Salinas image, Pavia University image, and Indian Pines image, the proposed method effectively improves the classification accuracy with less labeled samples.