Combination strategy of active learning for hyperspectral images classification - Ying CUI; Kai XU; Zhongjun LU; Shubin LIU; Liguo WANG (Tongxin xuebao)
In order to improve the phenomena of jitter and instability of the traditional active learning single strategy algorithm in selecting the most valuable unlabeled samples.The idea of weighted combination of ensemble learning classifier and proposes a joint selection based on the combination strategy method (ESAL,ensemble strategy active learning) was introduced,the combination of the model was extended to the combination of the strategy so as to achieve the fusion of multiple strategies in a single model and achieve higher stability.By analyzing the classification results of hyperspectral remote sensing images,the ESAL algorithm can save 25.4% of the cost compared with the single strategy algorithm and reduce the jitter frequency to 16.67% when the same accuracy threshold is obtained,and the jitter is obviously improved.ESAL algorithm is out of good stability.