A Multi-Scale Convolution and Multi-Layer Fusion Network for Remote Sensing Forest Tree Species Recognition

Abstract

Forest tree species identification in the field of remote sensing has become an important research topic. Currently, few research methods combine global and local features, making it challenging to accurately handle the similarity between different categories. Moreover, using a single deep layer for feature extraction overlooks the unique feature information at intermediate levels. This paper proposes a remote sensing image forest tree species classification method based on the Multi-Scale Convolution and Multi-Level Fusion Network (MCMFN) architecture. In the MCMFN network, the Shallow Multi-Scale Convolution Attention Combination (SMCAC) module replaces the original 7 × 7 convolution at the first layer of ResNet-50. This module uses multi-scale convolution to capture different receptive fields, and combines it with the attention mechanism to effectively enhance the ability of shallow features and obtain richer feature information. Additionally, to make efficient use of intermediate and deep-level feature information, the Multi-layer Selection Feature Fusion (MSFF) module is employed to improve classification accuracy. Experimental results on the Aerial forest dataset demonstrate a classification accuracy of 91.03%. The comprehensive experiments indicate the feasibility and effectiveness of the proposed MCMFN network.