Orientation guided anchoring for geospatial object detection from remote sensing imagery
Orientation guided anchoring for geospatial object detection from remote sensing imagery
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DOI：
10.1016/j.isprsjprs.2019.12.001
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发表时间：
2020-02
影响因子：
12.7
通讯作者：
Yongtao Yu;H. Guan;Dilong Li;Tiannan Gu;E. Tang;Aixia Li
中科院分区：
文献类型：
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作者：
Yongtao Yu;H. Guan;Dilong Li;Tiannan Gu;E. Tang;Aixia Li
遥感图像中的目标检测在城市规划、智能交通系统、生态环境分析等领域有着广泛的应用。然而，尺度变化、方位变化、光照变化、部分遮挡以及图像质量给精确的地理空间目标检测带来了巨大的挑战。本文提出了一种基于卷积神经网络的基于方位引导锚定的地理空间目标检测网络。为了处理不同大小的对象，特征提取子网络提取不同尺度上的语义强特征的金字塔。基于定向导向锚定，锚定生成子网络生成一组高质量的定向锚定作为对象建议。在方向感兴趣区域汇集之后，通过目标检测子网络从目标建议中检测感兴趣的目标。该方法已经在一个大型地理空间目标检测数据集上进行了测试。定量评估表明，总体完备性、正确性、质量和F1度量分别为0.9232、0.9648、0.8931和0.9435。此外，该方法在云计算平台上的GPU上实现了每秒8幅图像的处理速度。与现有目标检测方法的对比研究也证明了该方法具有较高的检测精度和计算效率。
Object detection from remote sensing imagery plays a significant role in a wide range of applications, including urban planning, intelligent transportation systems, ecology and environment analysis, etc. However, scale variations, orientation variations, illumination changes, and partial occlusions, as well as image qualities, bring great challenges for accurate geospatial object detection. In this paper, we propose an efficient orientation guided anchoring based geospatial object detection network based on convolutional neural networks. To handle objects of varying sizes, the feature extraction subnetwork extracts a pyramid of semantically strong features at different scales. Based on orientation guided anchoring, the anchor generation subnetwork generates a small set of high-quality, oriented anchors as object proposals. After orientation region of interest pooling, objects of interest are detected from the object proposals through the object detection subnetwork. The proposed method has been tested on a large geospatial object detection dataset. Quantitative evaluations show that an overall completeness, correctness, quality, and F1-measure of 0.9232, 0.9648, 0.8931, and 0.9435, respectively, are obtained. In addition, the proposed method achieves a processing speed of 8 images per second on a GPU on the cloud computing platform. Comparative studies with the existing object detection methods also demonstrate the advantageous detection accuracy and computational efficiency of our proposed method.