Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Sharifzadeh, Sara; Tata, Jagati; Sharifzadeh, Hilda; Tan, Bo

doi:10.1007/978-3-030-54595-6_7

Cited by 9 publications

(9 citation statements)

References 50 publications

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“…However, there are a variety of semantic segmentation models such as U-Net, FCN, SegNet, PSPNet, and DeepLab series, etc. Among the various semantic segmentation models, DeepLabV3+ had been extensively used in many studies [18][19][20] and can obtain more accurate contours. Therefore, DeepLabV3+ was chosen as the model for the recognition of grapes.…”

Section: Motivationmentioning

confidence: 99%

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

2021

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Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding region of the grape in the depth image captured by RealSense D435 was processed by the proposed depth region growing algorithm (DRG) to extract the front cluster. The depth region growing method uses the depth value instead of gray value to achieve clustering. Finally, it fils the holes in the clustered region of interest, extracts the contours, and maps the obtained contours to the RGB image. The images captured by RealSense D435 in a natural trellis environment were adopted to evaluate the performance of the proposed method. The experimental results showed that the recall and precision of the proposed method were 89.2% and 87.5%, respectively. The demonstrated performance indicated that the proposed method could satisfy the requirements of practical application for robotic grape harvesting.

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Section: Motivationmentioning

confidence: 99%

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

2021

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“…Another criterion that is used for the proposed semisupervised frame work is the dissimilarity of the segmented area to the labelled patches. This shown in (2), where is the total number of clusters and is the total number of segments clustered into cluster and is the 13 length feature vectors of the segment of cluster and is the feature vector of cluster .…”

Section: F Evaluation Of Segmentationmentioning

confidence: 99%

“…It finds application in many domains. Examples are urban data analysis and planning [1], digital agriculture [2,3], environmental studies and hazards analysis [4] and traffic and navigation [5].…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Semi-Supervised Segmentation of Satellite Images

Sharifzadeh

Amiri

Abdi

2021

2021 22nd IEEE International Conference on Industrial Technology (ICIT)

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Satellite image segmentation is an important topic in many domains. This paper introduces a novel semisupervised image segmentation method for satellite image segmentation. Unlike the semantic segmentation strategies, this method requires only limited labelled data from small local patches of satellite images. Due to the complexity and large number of land cover objects in satellite images, a fixed-size square window is used for feature extraction from local areas. Having the features from the labelled patches, the suitability of the window scale is found efficiently. Furthermore, the labeled features remove the need for iterative clustering for decision making about features. The labelled data also allows learning a subspace of transformed features for segmentation of water and green area based on simple thresholding. Comparison of the segmentation results using the proposed strategy compared to unsupervised techniques such as k-means clustering and Superpixel-based Fast Fuzzy C-Means Clustering (SFFCM) shows the superiority of the proposed strategy in terms of content-based segmentation.

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“…Deep convolutional neural network algorithms learn different image features by building a hierarchy of data representation, which make this type of algorithm very efficient in processing complex real-world images such as remote sensing images. Thus, deep convolutional neural networks have been applied to several tasks in extracting information from satellite and aerial images such as road extraction [8,9], farm segmentation [10], building footprint segmentation [11][12][13][14] and building damage assessment [15,16]. These existing research studies have shown that the end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction in comparison to more traditional methods that are based on the features engineering approach.…”

Section: Introductionmentioning

confidence: 99%

Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

Touzani

Granderson

2021

Remote Sensing

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Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.

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Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 9 publications

References 50 publications

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

A New Method for Semi-Supervised Segmentation of Satellite Images

Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

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