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Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Remote Sens. 2019, 11(3), 272; https://doi.org/10.3390/rs11030272
Received: 3 December 2018 / Revised: 21 January 2019 / Accepted: 25 January 2019 / Published: 30 January 2019
(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)
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Abstract

In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-class object detection method with a convolutional neural network (CNN), which can be divided into two parts: a class-specific anchor based region proposal network (RPN) and a discriminative feature with a context information classification network. A class-specific anchor block providing better initial values for RPN is proposed to generate the anchor of the most suitable scale for each category in order to increase the recall ratio. Meanwhile, we proposed to incorporate the context information into the original convolutional feature to improve the discriminative ability of the features and increase classification accuracy. Considering the quality of samples for classification, the soft filter is proposed to select effective boxes to improve the diversity of the samples for the classifier and avoid missing or false detection to some extent. We also introduced the focal loss in order to improve the classifier in classifying the hard samples. The proposed method is tested on a benchmark dataset of ten classes to prove the superiority. The proposed method outperforms some state-of-the-art methods with a mean average precision (mAP) of 90.4% and better detects the multi-scale objects, especially when objects show a minor shape change. View Full-Text
Keywords: multi-scale geospatial object detection; class-specific anchor; discriminative feature with context information; focal loss; soft filter multi-scale geospatial object detection; class-specific anchor; discriminative feature with context information; focal loss; soft filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mo, N.; Yan, L.; Zhu, R.; Xie, H. Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network. Remote Sens. 2019, 11, 272.

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