Abstract
Multi-source data remote sensing provides innovative technical support for tree species recognition. Tree species recognition is relatively poor despite noteworthy advancements in image fusion methods because the features from multi-source data for each pixel in the same region cannot be deeply exploited. In the present paper, a novel deep learning approach for hyperspectral imagery is proposed to improve accuracy for the classification of tree species. The proposed method, named the double branch multi-source fusion (DBMF) method, could more deeply determine the relationship between multi-source data and provide more effective information. The DBMF method does this by fusing spectral features extracted from a hyperspectral image (HSI) captured by the HJ-1A satellite and spatial features extracted from a multispectral image (MSI) captured by the Sentinel-2 satellite. The network has two branches in the spatial branch to avoid the risk of information loss, of which, sandglass blocks are embedded into a convolutional neural network (CNN) to extract the corresponding spatial neighborhood features from the MSI. Simultaneously, to make the useful spectral feature transfer more effective in the spectral branch, we employed bidirectional long short-term memory (Bi-LSTM) with a triple attention mechanism to extract the spectral features of each pixel in the HSI with low resolution. The feature information is fused to classify the tree species after the addition of a fusion activation function, which could allow the network to obtain more interactive information. Finally, the fusion strategy allows for the prediction of the full classification map of three study areas. Experimental results on a multi-source dataset show that DBMF has a significant advantage over other state-of-the-art frameworks.
Highlights
The proposed method for tree species data classification was compared with the traditional classifier support vector machines (SVM) [41], as well as recently developed convolutional neural network (CNN)-type methods, contextual deep CNN (CD-CNN) [27], double-branch multi-attention mechanism network (DBMA) [42], and double-branch dual-attention mechanism network (DBDA) [43]
Because SVM does not belong to deep learning (DL), only the other four algorithms were learned in the training process
All the methods were tested on a single hyperspectral image (HSI), single multispectral image (MSI), and fused HSI and MSI with data resolution upscaled to the same pixel level
Summary
Accurate tree species distribution information is crucial for effective forest resource protection efforts. The wide range and large area distribution of forests make it excessively time-consuming and labor-intensive to manually identify the tree species they contain. There is a demand for an effective classification framework to recognize the distribution information of large-area tree species [2]. Spectrally rich, continuous spatial, and multi-temporal information, allowing tree species to be identified on the basis of their spectral and structural characteristics. Satellite remote sensing sensor technology is rapidly changing, the high-resolution information of forests requires new classification algorithms to bridge the gap between their needs and the wealth of data information. The present study aimed to improve existing automatic tree species classification techniques [3]
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