Tree Species Classification Based on ASDER and MALSTM-FCN

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 7; p. 1723
• Main Authors: Luo, Hongjian, Ming, Dongping, Xu, Lu, Ling, Xiao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Agricultural production; Analysis; ASDER; attention mechanism; Classification; Computational linguistics; Deep learning; Ecological evaluation; Ecological monitoring; Entropy; Environmental management; Environmental monitoring; Environmental protection; feature optimization; Food; Forest management; Language processing; Long short-term memory; Machine learning; Natural language interfaces; Neural networks; Optimization; Optimization algorithms; Optimization techniques; Packaging; Plant species; Remote sensing; Species; Species classification; Straight lines; Sustainable forestry; time-series classification; tree species classification; China; Beijing China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15071723

Tree species classification based on multi-source remote sensing data is essential for ecological evaluation, environmental monitoring, and forest management. The optimization of classification features and the performance of classification methods are crucial to tree species classification. This paper proposes Angle-weighted Standard Deviation Elliptic Cross-merge Rate (ASDER) as a separability metric for feature optimization. ASDER uses mutual information to represent the separability metric and avoids the difficulty of differentiation caused by multiple ellipse centers and coordinate origins forming straight lines by angle weighting. In classification method, Multi-head Self-attention Long Short-Term Memory—Full Convolution Network (MALSTM-FCN) is constructed in this paper. MALSTM-FCN enhances the global correlation in time series and improves classification accuracy through a multi-head self-attention mechanism. This paper takes Beijing Olympic Forest Park (after this, referred to as Aosen) as the research area, constructs a tree species classification dataset based on an actual ground survey, and obtains a classification accuracy of 95.20% using the above method. This paper demonstrates the effectiveness of ASDER and MALSTM-FCN by comparing temporal entropy and LSTM-FCN and shows that the method has some practicality for tree species classification.