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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Vehicle Detection in Remote Sensing Images Leveraging on Simultaneous Super-Resolution
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Vehicle Detection in Remote Sensing Images Leveraging on Simultaneous Super-Resolution

机译：遥感图像中的车辆检测在同时超级分辨率下利用

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摘要

Owing to the relatively small size of vehicles in remote sensing images, lacking sufficient detailed appearance to distinguish vehicles from similar objects, the detection performance is still far from satisfactory compared with the detection results on everyday images. Inspired by the positive effects of super-resolution convolutional neural network (SRCNN) for object detection and the stunning success of deep CNN techniques, we apply generative adversarial network frameworks to realize simultaneous SRCNN and vehicle detection in an end-to-end manner, and the detection loss is backpropagated into the SRCNN during training to facilitate detection. In particular, our work is unsupervised and bypasses the requirement of low-/high-resolution image pairs during the training stage, achieving increased generality and applicability. Extensive experiments on representative data sets demonstrate that our method outperforms the state-of-the-art detectors. (The source code will be made available after the review process.)
机译：由于遥感图像中的车辆尺寸相对较小，缺乏足够的详细外观来区分车辆从类似的物体区分车辆，与日常图像上的检测结果相比，检测性能仍然远离令人满意。灵感来自超分辨率卷积神经网络（SRCNN）对物体检测的积极影响和深层CNN技术的令人惊叹的成功，我们应用生成的对抗网络框架以端到端的方式实现同​​时SRCNN和车辆检测，在训练期间，检测丢失在SRCNN中被反向化，以便于检测。特别是，我们的工作是无监督，绕过训练阶段的低/高分辨率图像对的要求，实现了增加的一般性和适用性。关于代表性数据集的广泛实验表明，我们的方法优于最先进的探测器。 （审查过程后，源代码将提供。）

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