Spatial-Temporal Hidden Markov Model for Land Cover Classification Using Multitemporal Satellite Images

Land cover is of great significance for the study of global ecological environmental change. Multitemporal land cover can help us to understand the change process of the regional environment and formulate corresponding protection policies. For single-period image classification, the spatial-temporal information is often ignored, and the classification accuracy is difficult to improve. In this paper, an iterative hidden Markov model (STHMM) is proposed to optimize the multitemporal classification, in which a stacked autoencoding classifier is used to calculate the initial class probability, and the extended random walker-based spatial optimization technique is adopted to optimize the class probability. Finally, the hidden Markov model with expectation maximization is built by exploiting postprocessing temporal optimization. Experimental results show that the proposed method can outperform other classification techniques, and the spatial-temporal hidden Markov model proposed in this paper exhibits more stable and reliable performance and can be widely used in multitemporal classification.