Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
|Place of Conferral||中科院自动化所|
|Keyword||目标检测 光学遥感图像 深度学习 尺度不变性 旋转不变性|
Object detection is an important research direction in the field of remote sensing image processing and analysis. It is widely used in resources exploration, port monitoring, traffic dredging and other military and civilian fields. With the progress of earth observation technology, there are more and more ways to obtain high-resolution remote sensing images. At the same time, it is more and more urgent to extract valuable information from massive remote sensing images, which poses a great challenge to remote sensing object detection. The traditional object detection methods based on manually designed features have been unable to meet the current complex image processing requirements due to its poor robustness and low detection accuracy. In recent years, deep learning based on deep neural network, through stacking multi-layer neural network and extracting abstract features layer by layer, has strong feature representation ability. In many natural image recognition tasks, the method based on deep learning has shown strong generalization ability, and its performance is much better than that based on traditional features. However, due to the complexities of remote sensing images, such as significant differences with respect to object sizes and rotation variations, traditional deep learning-based object detection approaches are inadequate for remote sensing images, and practical applications are thus being hindered. In this context, this dissertation aims to study object detection based on deep learning technology by focusing on the characteristics of remote sensing images. The main contents and contributions of this dissertation are as follows:
1. An instance scale normalization method is proposed to solve the problem of large scale variation of objects. By normalizing all objects into a predefined smaller scale range for training and testing, this method can eliminate the impacts of large scale variations on detection performance. In order to preserve the diversity of other features and accelerate the training of large-size remote sensing images, this dissertation proposes a method combining image pyramid and greedy patch generation to achieve flexible instance scale normalization. The effectiveness of the proposed method is verified in remote sensing image object detection task. The generalization is also verified on several instance related recognition tasks in natural image. A multi-scale remote sensing image object detection algorithm is proposed based on instance scale normalization and feature pyramid network and achieves the state-of-the-art accuracy on public dataset.
2. To address the problem of multi-rotation of target in remote sensing image, two approaches are proposed in different perspectives. One is that, the deformable convolution based rotation-invariant object detection method, where the ordinary convolution is replaced by deformable convolution for advanced horizontal bounding box object detection in remote sensing images. Deformable convolution can adaptively adjust the sampling position of convolution according to the image content and extract rotation invariant features. In the training process, the sampling offset of deformable convolution is initialized to be 0 to reduce the difficulty of network training. Experiments show that the use of deformable convolution at high level of ResNet is more useful for remote sensing object detection. Secondly, to obtain more accurate location and direction of the object, the FRCNN-OBB algorithm is proposed for rotating bounding box object detection. In the first stage, the region proposal network only regresses to the minimum external horizontal rectangular box of the object, and the candidate boxes are generated. In the second stage of regression, considering the problem that the regression path is too long after the candidate box is matched with the
|黄河. 基于深度学习的遥感图像目标检测技术研究[D]. 中科院自动化所. 中科院自动化所,2019.|
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