卫星
遥感
边界(拓扑)
比例(比率)
计算机科学
卫星图像
深度学习
卷积(计算机科学)
人工智能
计算机视觉
特征(语言学)
卷积神经网络
块(置换群论)
图像分辨率
鉴定(生物学)
编码器
模式识别(心理学)
数据挖掘
地理
地图学
人工神经网络
工程类
数学
操作系统
植物
几何学
生物
航空航天工程
哲学
语言学
数学分析
作者
Wang Shunying,Zhou Yinqing,Yang Xianzeng,Feng Li,Tianjun Wu,Jiancheng Luo
标识
DOI:10.1016/j.compag.2023.107683
摘要
Mapping farmland parcels using satellite images is essential for agricultural remote sensing applications. Loss of spatial details and positioning of parcel boundaries are the main challenges in available deep convolution network (DCN) models. This study developed a boundary-semantic-fusion DCN (BSNet) model for delineating farmland parcels from high-resolution satellite images. Central to this method is the combination between shallow-level boundary features with accurate spatial positioning and deep-level semantic features for category identification. First, a general deep convolution framework consisting of boundary, semantic and fusion blocks was implemented for farmland parcel mapping. Second, a particular serial structure with a detaching operation for linking the boundary and semantic blocks was explored to maintain the spatial details and fine-scale boundaries in feature learning. Third, an encoder-decoder fusion block was developed to integrate the boundary and semantic features to produce the final parcel maps. We validated the proposed model with different high-resolution satellite images in two study areas. The experimental results, with improvements greater than 4% in the F1 score and 6% in the IoU score relative to other comparative methods, illustrate the effectiveness of the proposed model for fine-scale farmland parcel mapping.
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