Corpus ID: 231839856

A Deep Learning Approach Based on Graphs to Detect Plantation Lines

  title={A Deep Learning Approach Based on Graphs to Detect Plantation Lines},
  author={Diogo Nunes Gonçalves and Mauro dos Santos de Arruda and Hemerson Pistori and Vanessa Jord{\~a}o Marcato Fernandes and Ana Paula Marques Ramos and Danielle Elis Garcia Furuya and Lucas Prado Osco and Hongjie He and Jonathan Li and Jos{\'e} Marcato Junior and Wesley Nunes Gonçalves},
Identifying plantation lines in aerial images of agricultural landscapes is required for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery presenting a challenging… Expand


A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery
The convolutional neural network approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchard trees. Expand
Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images
This paper proposes a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images that is comparable to traditional supervised training data labeling. Expand
Remote sensing scene classification based on high-order graph convolutional network
ABSTRACT Remote sensing scene classification has gained increasing interest in remote sensing image understanding and feature representation is the crucial factor for classification methods.Expand
A graph convolutional neural network for classification of building patterns using spatial vector data
A novel graph convolution is introduced by converting it from the vertex domain into a point-wise product in the Fourier domain using the graph Fourier transform and convolution theorem, which achieves a significant improvement over existing methods. Expand
Remote Sensing Image Classification Based on a Cross-Attention Mechanism and Graph Convolution
A novel cross-attention mechanism and graph convolution integration algorithm is proposed in this letter that achieves better performances than do other well-known algorithms using different methods of training set division. Expand
Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net
The experimental results demonstrate the superiority of the proposed RCNN-UNet model for both the road detection and the centerline extraction tasks, and a multitask learning scheme is designed so that two predictors can be simultaneously trained to improve both effectiveness and efficiency. Expand
Combining Deep Semantic Segmentation Network and Graph Convolutional Neural Network for Semantic Segmentation of Remote Sensing Imagery
A new DSSN called the attention residual U-shaped network (AttResUNet), which leverages residual blocks to encode feature maps and the attention module to refine the features, is proposed in this paper for RS image semantic segmentation. Expand
River segmentation based on separable attention residual network
In this method, residual neural network is used as the backbone network to obtain the information features of rivers, and the deep feature information is fused with the shallow feature information through attention modules of different scales. Expand
A Review on Deep Learning in UAV Remote Sensing
This revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts. Expand
Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data
Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches and its computation time is also far less than the current mainstream pixel-level semantic segmentation networks. Expand