Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Sara Sharifzadeh, Jagati Tata, Hilda Sharifzadeh, Bo Tan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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Abstract

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.
Original languageEnglish
Title of host publicationData Management Technologies and Applications
EditorsSlimane Hammoudi, Christoph Quix, Christoph Quix, Jorge Bernardino
PublisherSpringer
Pages115-135
Number of pages21
ISBN (Electronic)978-3-030-54595-6
ISBN (Print)978-3-030-54594-9
DOIs
Publication statusE-pub ahead of print - 30 Jul 2020
EventInternational Conference on Data Management Technologies and Applications - Prague, Czech Republic
Duration: 26 Jul 201928 Jul 2019

Publication series

NameCommunications in Computer and Information Science
Volume1255 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceInternational Conference on Data Management Technologies and Applications
CountryCzech Republic
CityPrague
Period26/07/1928/07/19

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-54595-6_7

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

  • Farm detection
  • Satellite image
  • Semantic segmentation

ASJC Scopus subject areas

  • Mathematics(all)
  • Computer Science(all)

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