Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China

被引:20
作者
Lu, Rui [1 ]
Wang, Nan [1 ]
Zhang, Yanbin [2 ]
Lin, Yeneng [3 ]
Wu, Wenqiang [1 ]
Shi, Zhou [1 ]
机构
[1] Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applic, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China
[2] Consolidat & Rehabil Ctr, Hangzhou 310007, Peoples R China
[3] Inst Cyber Syst & Control, Yuquan Campus, Hangzhou 310027, Zhejiang, Peoples R China
关键词
agricultural field extraction; attention mechanism; deep learning; GaoFen-2 (GF-2); multi-scale feature fusion; CONVOLUTIONAL NEURAL-NETWORKS; REMOTE-SENSING IMAGES; LAND-COVER; SEMANTIC SEGMENTATION; CLOUD DETECTION; LEARNING ALGORITHMS; CLASSIFICATION; METAANALYSIS; FRAMEWORK; DENSITY;
D O I
10.3390/rs14092253
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Agricultural fields are essential in providing human beings with paramount food and other materials. Quick and accurate identification of agricultural fields from the remote sensing images is a crucial task in digital and precision agriculture. Deep learning methods have the advantages of fast and accurate image segmentation, especially for extracting the agricultural fields from remote sensing images. This paper proposed a deep neural network with a dual attention mechanism and a multi-scale feature fusion (Dual Attention and Scale Fusion Network, DASFNet) to extract the cropland from a GaoFen-2 (GF-2) image of 2017 in Alar, south Xinjiang, China. First, we constructed an agricultural field segmentation dataset from the GF-2 image. Next, seven evaluation indices were selected to assess the extraction accuracy, including the location shift, to reveal the spatial relationship and facilitate a better evaluation. Finally, we proposed DASFNet incorporating three ameliorated and novel deep learning modules with the dual attention mechanism and multi-scale feature fusion methods. The comparison of these modules indicated their effects and advantages. Compared with different segmentation convolutional neural networks, DASFNet achieved the best testing accuracy in extracting fields with an F1-score of 0.9017, an intersection over a union of 0.8932, a Kappa coefficient of 0.8869, and a location shift of 1.1752 pixels. Agricultural fields can be extracted automatedly and accurately using DASFNet, which reduces the manual record of the agricultural field information and is conducive to further farmland surveys, protection, and management.
引用
收藏
页数:23
相关论文
共 75 条
  • [1] Climate change and eastern Africa: a review of impact on major crops
    Adhikari, Umesh
    Nejadhashemi, A. Pouyan
    Woznicki, Sean A.
    [J]. FOOD AND ENERGY SECURITY, 2015, 4 (02): : 110 - 132
  • [2] SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
    Badrinarayanan, Vijay
    Kendall, Alex
    Cipolla, Roberto
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) : 2481 - 2495
  • [3] Crop segmentation from images by morphology modeling in the CIE L*a*b* color space
    Bai, X. D.
    Cao, Z. G.
    Wang, Y.
    Yu, Z. H.
    Zhang, X. F.
    Li, C. N.
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2013, 99 : 21 - 34
  • [4] DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images
    Chen, Jie
    Yuan, Ziyang
    Peng, Jian
    Chen, Li
    Huang, Haozhe
    Zhu, Jiawei
    Liu, Yu
    Li, Haifeng
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 1194 - 1206
  • [5] Chen LB, 2017, IEEE INT SYMP NANO, P1, DOI 10.1109/NANOARCH.2017.8053709
  • [6] Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images
    Chen, Ziyi
    Wang, Cheng
    Li, Jonathan
    Xie, Nianci
    Han, Yan
    Du, Jixiang
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 2284 - 2294
  • [7] Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds
    Cheng, Zhenzhen
    Qi, Lijun
    Cheng, Yifan
    [J]. AGRICULTURE-BASEL, 2021, 11 (05):
  • [8] Contour Detection for UAV-Based Cadastral Mapping
    Crommelinck, Sophie
    Bennett, Rohan
    Gerke, Markus
    Yang, Michael Ying
    Vosselman, George
    [J]. REMOTE SENSING, 2017, 9 (02)
  • [9] An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery
    de Castro, Ana I.
    Torres-Sanchez, Jorge
    Pena, Jose M.
    Jimenez-Brenes, Francisco M.
    Csillik, Ovidiu
    Lopez-Granados, Francisca
    [J]. REMOTE SENSING, 2018, 10 (02)
  • [10] Multi-scale object detection in remote sensing imagery with convolutional neural networks
    Deng, Zhipeng
    Sun, Hao
    Zhou, Shilin
    Zhao, Juanping
    Lei, Lin
    Zou, Huanxin
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2018, 145 : 3 - 22