1 September 2017 DeepPlane: a unified deep model for aircraft detection and recognition in remote sensing images
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Abstract
Deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition from natural images. In contrast, object recognition from remote sensing images is more challenging, due to the complex background and inadequate data for training a deep network with a huge number of parameters. We propose a unified deep CNN, called DeepPlane, to simultaneously detect the position and classify the category of aircraft in remote sensing images. This model consists of two correlative deep networks: the first one is designed to generate object proposals as well as feature maps and the second one is cascaded upon the first one to perform classification and box regression in one shot. The “inception module” is introduced to tackle the insufficient training data problem that is one of the most challenging obstacles of detection in remote sensing images. Extensive experiments demonstrate the efficiency of the proposed DeepPlane model. Specifically, DeepPlane could model detection and classification jointly and achieves 91.9% mAP in six categories of aircraft, which advances the state-of-the-art, sometimes considerably, for both tasks.
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Hongzhen Wang, Yongchao Gong, Ying Wang, Lingfeng Wang, Chunhong Pan, "DeepPlane: a unified deep model for aircraft detection and recognition in remote sensing images," Journal of Applied Remote Sensing 11(4), 042606 (1 September 2017). https://doi.org/10.1117/1.JRS.11.042606 Submission: Received 21 February 2017; Accepted 3 August 2017
Submission: Received 21 February 2017; Accepted 3 August 2017
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