Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network
نویسندگان
چکیده
At present, forest and fruit resource surveys are mainly based on ground surveys, the information technology of characteristic industries is evidently lagging. The automatic extraction tree from massive remote sensing data critical for healthy development industries. However, complex spatial weak spectral contained in high-resolution images make it difficult to classify trees. In recent years, fully convolutional neural networks (FCNs) have been shown perform well semantic segmentation because their end-to-end network structures. this paper, an model, Multi-Unet, was constructed. As improved version U-Net structure, structure adopted multiscale convolution kernels learn under different receptive fields. addition, “spatial-channel” attention guidance module introduced fuse low-level high-level features reduce unnecessary refine classification results. proposed model tested a pear dataset constructed through field annotation work. results show that Multi-Unet best performer among all models, with accuracy, recall, F1, kappa coefficient 88.95%, 89.57%, 89.26%, 88.74%, respectively. This study provides important practical significance sustainable industry.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15133283