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Land Use/Land Cover (LULC) classification has become increasingly important in various fields, including ecological and environmental protection, urban planning, and geological disaster monitoring. With the development of high-resolution remote sensing satellite technology, there is a growing focus on achieving precise LULC classification. However, the accuracy of fine-grained LULC classification is challenged by the high intra-class diversity and low inter-class separability inherent in high-resolution remote sensing images. To address this challenge, this paper proposes a novel multi-path feature fusion semantic segmentation model, called MPFFNet, which combines the segmentation results of convolutional neural networks with traditional filtering processes to achieve finer LULC classification. MPFFNet consists of three modules: the Improved Encoder Module (IEM) extracts contextual and spatial detail information through the backbone network, DASPP, and MFEAM; the Improved Decoder Module (IDM) utilizes the Cascade Feature Fusion (CFF) module to effectively merge shallow and deep information; and the Feature Fusion Module (FAM) enables dual-path feature fusion using a convolutional neural network and Gabor Filter. Experimental results on the large-scale classification set and the fine land-cover classification set of the Gaofen Image Dataset (GID) demonstrate the effectiveness of the proposed method, achieving mIoU scores of 81.02% and 77.83%, respectively. These scores outperform U-Net by 7.95% and 3.28%, respectively. Therefore, we believe that our model can deliver superior results in the task of LULC classification.

MPFFNet: LULC classification model for high-resolution remote sensing images with multi-path feature fusion