Cropland recognition in southern hilly areas under a hybrid U-Net model

doi:10.13474/j.cnki.11-2246.2023.0359

Abstract

Abstract: In order to solve the difficulties and low accuracy of sloping cropland identification caused by scattered, fragmented and irregular cropland in southern hilly areas, based on the U-Net, the efficient channel attention(ECA) and the attention gate (AG) dual attention mechanism are introduced, and a hybrid U-Net model is proposed. This model is applied to extract cropland from WorldView-2 satellite images in Nan'an of Fujian province in 2021. Experiment shows that the hybrid U-shaped network model has achieved a good accuracy of 93.42%, which is better than a single-attention mechanism model (ECA U-Net and U-Net), and the accuracy has increased by 9.75% and 19% respectively. The average F1 scores of cropland in the hybrid U-Net model are 0.921 2, 0.902 5 and 0.932 2 respectively in the mountainous, semi hilly and plain test areas, especially in the mountainous and semi hilly areas. On this basis, the spatial distribution of cropland in Nan'an is analyzed, which provided effective technical support for abandoned hillside fields for grain cultivation, rational adjustment of slope cropland exceeding 25 degrees, and effective control of cropland quantity.

Key words: cropland, high-resolution image, attention mechanism, U-Net, southern hilly area

CLC Number:

WU Ruijiao. Cropland recognition in southern hilly areas under a hybrid U-Net model[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 57-62,111.

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