2022-02-01 null null 13(卷), null(期), (null页)
Semi-arid regions belong to important ecological transitional zones with a high ecosystem vulnerability and sensitivity. In recent years, due to the influence of climate change and human activities, the land covers of semi-arid regions have experienced dramatic changes. Therefore, timely and accurate land cover mapping of semi-arid regions is of great significance. Deep learning has been a hotspot in land cover mapping; however, it requires a large number of labelled samples. To tackle this data-hunger issue, we resolve to a multi-gate semi-supervised learning (SSL) method that could use a huge number of unlabelled samples to improve the classification performance. Specifically, the proposed method consists of a probability gate, an uncertainty gate and a consistency gate, aiming to generate high-quality pseudo-labels from the unlabelled datasets. Experimental results from Zhangbei, China indicate that the proposed method yields a good performance with an overall accuracy of 90.31%, which is 4.31% higher than that of traditional supervised learning (SL) methods. Ablation studies also verify the effectiveness of the gated mechanism in selecting unlabelled data.
关键词