State-of-the-art object detection networks have reduced the running time and get better detection results. However, for remote sensing image scenes, in a certain projection direction, the remote sensing image target will be tilted, The positioning of the horizontal bounding box used by the exit detection algorithm will cause a lot of overlap between the target bounding boxes, After using NMS (Non-maximum suppression),it will lead to the lost of the target. In this paper,we propose a Rotated Faster R-CNN(R-FRCNN) that is a target positioning method based on arbitrary angle bounding box,which can perform non-redundant positioning on the target,thus can reduce the missed detection rate. when the target is densely distributed or the angle of the object is arbitrary.Compared with traditional and state-ofthe art object detection algorithms,our approach obtain the superior performance.
With the increasing amount of high-resolution remote sensing images, large-scale remote sensing image retrieval(RSIR) becomes more and more significant and has attracted great attention. Traditional image retrieval methods generally use hand-crafted features which are not only time-consuming but also always get poor performance. Deep learning recently achieves remarkable performance due to its powerful ability to learn high-level semantic features, so researchers attempt to take advantage of features derived from Convolutional Neural Networks(CNNs) in RSIR. But remote sensing image is different from natural scene image, its background is more complicated with a lot of noise and existing deep learning method didn’t handle this well. Both the speed and the accuracy achieve unsatisfactory performance. In this paper, we propose a rotation invariant hashing network that represents an image as a binary hash code to retrieve image faster while considering the rotation invariance of the same target. The results of the experiments on some available remote sensing datasets show that our method is effective and outperforms than other features which is usually used in RSIR.
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.
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