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Grassland detection is essential for sustainable development, particularly in ecological and animal husbandry. Hyperspectral can retrieve the functional characteristics of vegetation species, nevertheless, the efficiency of grassland communities with rich species at different periods needs to be improved. In this study, the multi-temporal grass hyperspectral classification via full pixel decomposition spectral manifold projection and boosting active learning (FPD-SMP-BAL) model is proposed to relieve the above issue. Considering the plentiful spectral information of hyperspectral, we proposed a novel preprocessing method of spectral manifold projection based on full pixel decomposition (FPD-SMP), which reveals the internal nonlinear structure of global hyperspectral and alleviate the data redundancy. Furthermore, we innovatively designed the boosting active learning (BAL) classification model with an effective optimization strategy, which can decrease the cost of sample labeling by the minimal loss value. An on-site hyperspectral acquisition platform was assembled, and a dataset of 4,200 samples was established from the perspective of vegetation species composition and multi-stages to verify the validity of the model. Experimental results show that the proposed FPD-SMP-BAL framework reaches an overall accuracy of 94.94%, which is superior to some state-of-the-art methods in the classification of multi-temporal grass hyperspectral images, and also provides a reference for related research of grassland dynamic monitoring.

Multi-temporal grass hyperspectral classification via full pixel decomposition spectral manifold projection and boosting active learning model