High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.

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A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102086 ◽

2020 ◽

Vol 88 ◽

pp. 102086 ◽

Cited By ~ 8

Author(s):

Chenxiao Zhang ◽

Peng Yue ◽

Deodato Tapete ◽

Boyi Shangguan ◽

Mi Wang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Convolutional Neural Network ◽

Land Cover Classification ◽

Remote Sensing Images ◽

Object Based ◽

Multi Level ◽

Very High

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MSST-Net: A Multi-Scale Adaptive Network for Building Extraction from Remote Sensing Images Based on Swin Transformer

Remote Sensing ◽

10.3390/rs13234743 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4743

Author(s):

Wei Yuan ◽

Wenbo Xu

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Network Model ◽

Remote Sensing Images ◽

Feature Maps ◽

Global Features ◽

Adaptive Network ◽

Data Set ◽

Multi Scale

The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can supply each pixel with a global feature, makes up for the deficiency of the convolutional neural network. Therefore, a multi-scale adaptive segmentation network model (MSST-Net) based on a Swin Transformer is proposed in this paper. Firstly, a Swin Transformer is used as the backbone to encode the input image. Then, the feature maps of different levels are decoded separately. Thirdly, the convolution is used for fusion, so that the network can automatically learn the weight of the decoding results of each level. Finally, we adjust the channels to obtain the final prediction map by using the convolution with a kernel of 1 × 1. By comparing this with other segmentation network models on a WHU building data set, the evaluation metrics, mIoU, F1-score and accuracy are all improved. The network model proposed in this paper is a multi-scale adaptive network model that pays more attention to the global features for remote sensing segmentation.

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Deep convolutional neural network based large-scale oil palm tree detection for high-resolution remote sensing images

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2017.8127085 ◽

2017 ◽

Cited By ~ 2

Author(s):

Weijia Li ◽

Haohuan Fu ◽

Le Yu

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Convolutional Neural Network ◽

Oil Palm ◽

Large Scale ◽

Deep Convolutional Neural Network ◽

Palm Tree ◽

Remote Sensing Images ◽

Tree Detection

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Convolutional Neural Network for Building Extraction from High-Resolution Remote Sensing Images

2020 International Conference on Machine Vision and Image Processing (MVIP) ◽

10.1109/mvip49855.2020.9187483 ◽

2020 ◽

Author(s):

Hamidreza Hosseinpoor ◽

Farhad Samadzadegan

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Convolutional Neural Network ◽

Building Extraction ◽

Remote Sensing Images

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Semantic Segmentation of Remote Sensing Image Based on Convolutional Neural Network and Mask Generation

Mathematical Problems in Engineering ◽

10.1155/2021/2472726 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Binglin Niu

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Convolutional Neural Network ◽

Semantic Segmentation ◽

Layer By Layer ◽

Foreground Object ◽

Remote Sensing Images ◽

Training Time ◽

High Level

High-resolution remote sensing images usually contain complex semantic information and confusing targets, so their semantic segmentation is an important and challenging task. To resolve the problem of inadequate utilization of multilayer features by existing methods, a semantic segmentation method for remote sensing images based on convolutional neural network and mask generation is proposed. In this method, the boundary box is used as the initial foreground segmentation profile, and the edge information of the foreground object is obtained by using the multilayer feature of the convolutional neural network. In order to obtain the rough object segmentation mask, the general shape and position of the foreground object are estimated by using the high-level features in the process of layer-by-layer iteration. Then, based on the obtained rough mask, the mask is updated layer by layer using the neural network characteristics to obtain a more accurate mask. In order to solve the difficulty of deep neural network training and the problem of degeneration after convergence, a framework based on residual learning was adopted, which can simplify the training of those very deep networks and improve the accuracy of the network. For comparison with other advanced algorithms, the proposed algorithm was tested on the Potsdam and Vaihingen datasets. Experimental results show that, compared with other algorithms, the algorithm in this article can effectively improve the overall precision of semantic segmentation of high-resolution remote sensing images and shorten the overall training time and segmentation time.

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Intelligent High-Resolution Geological Mapping Based on SLIC-CNN

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020099 ◽

2020 ◽

Vol 9 (2) ◽

pp. 99

Author(s):

Xuejia Sang ◽

Linfu Xue ◽

Xiangjin Ran ◽

Xiaoshun Li ◽

Jiwen Liu ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Rock Mass ◽

High Resolution ◽

Convolutional Neural Network ◽

Area Under The Curve ◽

Mapping Method ◽

Geological Mapping ◽

Liaoning Province ◽

Remote Sensing Images

High-resolution geological mapping is an important supporting condition for mineral and energy exploration. However, high-resolution geological mapping work still faces many problems. At present, high-resolution geological mapping is still generated by expert interpretation of survey lines, compasses, and field data. The work in the field is constrained by the weather, terrain, and personnel, and the working methods need to be improved. This paper proposes a new method for high-resolution mapping using Unmanned Aerial Vehicle (UAV) and deep learning algorithms. This method uses the UAV to collect high-resolution remote sensing images, cooperates with some groundwork to anchor the lithology, and then completes most of the mapping work on high-resolution remote sensing images. This method transfers a large amount of field work into the room and provides an automatic mapping process based on the Simple Linear Iterative Clustering-Convolutional Neural Network (SLIC-CNN) algorithm. It uses the convolutional neural network (CNN) to identify the image content and confirms the lithologic distribution, the simple linear iterative cluster (SLIC) algorithm can be used to outline the boundary of the rock mass and determine the contact interface of the rock mass, and the mode and expert decision method is used to clarify the results of the fusion and mapping. The mapping method was applied to the Taili waterfront in Xingcheng City, Liaoning Province, China. In this study, the Area Under the Curve (AUC) of the mapping method was 0.937. The Kappa test result was k = 0.8523, and a high-resolution geological map was obtained.

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