IEEE Trans Image Process. 2018 May;27(5):2354-2367. doi: 10.1109/TIP.2018.2799324.
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using -expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.
本文提出了一种新的有监督分类算法，用于遥感高光谱图像（HSI），该算法将光谱和空间信息集成到统一的贝叶斯框架中。首先，我们从贝叶斯的角度来表述 HSI 分类问题。然后，我们采用卷积神经网络（CNN）通过基于补丁的训练策略来学习后验类分布，以更好地利用空间信息。接下来，通过对标签施加空间平滑先验来进一步考虑空间信息。最后，我们使用随机梯度下降迭代更新 CNN 参数，并使用基于 -膨胀最小割的算法更新所有像素向量的类标签。与其他最先进的方法相比，在许多实验设置中，该分类方法在一个合成数据集和两个基准 HSI 数据集上都取得了更好的性能。
