NIR-RGB-M<sup>2</sup>Net: A Fusion Model for Precise Agricultural Field Segmentation Using Multisource Remote Sensing Data

NIR-RGB-M<sup>2</sup>Net: A Fusion Model for Precise Agricultural Field Segmentation Using Multisource Remote Sensing Data

Precise extraction of agricultural field parcels is critical for resource management and yield prediction. Multisource remote sensing combines near-infrared (NIR) and visible light (RGB) data to leverage complementary features, but fusing these modalities often requires complex networks that risk lo...

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Bibliographic Details
Main Authors: Zhankui Tang, Xin Pan, Xiangfei She, Jian Zhao
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Agricultural segmentation fusion model
convolutional block attention module (CBAM)
dilated convolution module (DCM)
multisource remote sensing data fusion
precise agricultural field extraction
Online Access:https://ieeexplore.ieee.org/document/11105414/
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