A contextual deep neural network with dilated convolutions for object detection in remote sensing images

Shouhong Wan; Xingyue Li; Peiquan Jin; Chang Zou

doi:10.1117/12.2502866

9 August 2018 A contextual deep neural network with dilated convolutions for object detection in remote sensing images

Shouhong Wan, Xingyue Li, Peiquan Jin, Chang Zou

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108060W (2018) https://doi.org/10.1117/12.2502866
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Object detection is one of the most important issues in the field of remote sensing analysis. The lack of semantic information about objects poses difficulty for traditional methods in exploring effective features for object discrimination. Being capable of feature extraction, a series of region-based convolutional neural networks (R-CNN) have been widely and successfully applied for object detection in natural images recently. However, most of them suffer from the poor detection performance of small-sized targets, which means that few of them can be introduced directly for small-sized object detection in remote sensing images. This paper proposes a modified method based on faster R-CNN, which is composed of a feature extraction network, a region proposal network and an object detection network. Compared to faster R-CNN, in the feature extraction network, the proposed method removes the forth pooling layer and employs dilated convolutions on the all subsequent convolutional layers to enhance the resolution of the final feature maps, which provide more detailed and semantic feature information of targets to help detect objects especially the small-sized one. In the object detection network, contextual features around the region proposals are added as complement feature information to help distinguish objects accurately. Experiments conducted on two data sets verify that our proposal obtains a superior performance on small-sized object detection in remote sensing images.

Citation Download Citation

Shouhong Wan, Xingyue Li, Peiquan Jin, and Chang Zou "A contextual deep neural network with dilated convolutions for object detection in remote sensing images", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060W (9 August 2018); https://doi.org/10.1117/12.2502866

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