E-viri
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Haigang Zhu; Xiaogang Chen; Weiqun Dai; Kun Fu; Qixiang Ye; Jianbin Jiao
2015 IEEE International Conference on Image Processing (ICIP), 09/2015Conference Proceeding
Detecting objects in aerial images is challenged by variance of object colors, aspect ratios, cluttered backgrounds, and in particular, undetermined orientations. In this paper, we propose to use Deep Convolutional Neural Network (DCNN) features from combined layers to perform orientation robust aerial object detection. We explore the inherent characteristics of DC-NN as well as relate the extracted features to the principle of disentangling feature learning. An image segmentation based approach is used to localize ROIs of various aspect ratios, and ROIs are further classified into positives or negatives using an SVM classifier trained on DCNN features. With experiments on two datasets collected from Google Earth, we demonstrate that the proposed aerial object detection approach is simple but effective.
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Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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