The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-M-3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 155–161, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-155-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 155–161, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-155-2021

  10 Aug 2021

10 Aug 2021

IDENTIFYING OIL PADS IN HIGH SPATIAL RESOLUTION AERIAL IMAGES USING FASTER R-CNN

A. Sunil1, V. V. Sajithvariyar1, V. Sowmya1, R. Sivanpillai2, and K. P. Soman1 A. Sunil et al.
  • 1Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
  • 2Wyoming GIS Center, University of Wyoming, Laramie, WY 82072, USA

Keywords: Deep learning, Object detection, Faster R-CNN, Computer vision

Abstract. Deep learning (DL) methods are used for identifying objects in aerial and ground-based images. Detecting vehicles, roads, buildings, and crops are examples of object identification applications using DL methods. Identifying complex natural and man-made features continues to be a challenge. Oil pads are an example of complex built features due to their shape, size, and presence of other structures like sheds. This work applies Faster Region-based Convolutional Neural Network (R-CNN), a DL-based object recognition method, for identifying oil pads in high spatial resolution (1m), true-color aerial images. Faster R-CNN is a region-based object identification method, consisting of Regional Proposal Network (RPN) that helps to find the area where the target can be possibly present in the images. If the target is present in the images, the Faster R-CNN algorithm will identify the area in an image as foreground and the rest as background. The algorithm was trained with oil pad locations that were manually annotated from orthorectified imagery acquired in 2017. Eighty percent of the annotated images were used for training and the number of epochs was increased from 100 to 1000 in increments of 100 with a fixed length of 1000. After determining the optimal number of epochs the performance of the algorithm was evaluated with an independent set of validation images consisting of frames with and without oil pads. Results indicate that the Faster R-CNN algorithm can be used for identifying oil pads in aerial images.