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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery
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Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery

机译：基于遥感图像的两相方法准确地检测机动目标

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摘要

Maneuvering target detection in satellite images is difficult due to their small sizes, blurred appearances under various illuminations and shadows, and occlusion by trees and buildings. Recently, a fully convolutional regression network (FCRN) was proposed and achieved by the state-of-the-art performance in the Munich vehicle database. However, such a one-phase approach often makes mistakes at difficult places because of its swift glance and rejecting any second check. In this letter, a new object spatial density building net (SDBN) was designed, and a two-phase detection approach was proposed. It used the first SDBN to generate candidate regions and the second SDBN to proceed with a meticulous check on the object categories. Experiments on four maneuvering target databases, the Munich vehicle database, the Open Vehicle Database of San-Francisco (OVDS), the Overhead Imagery Research Data Set (OIRDS), and the Open Aircraft Database (OAD) show that the proposed method outperforms FCRN by an obvious margin. In addition, the accurate geometrical parameters (positions, orientations, and lengths) of all the objects were computed based on the spatial density maps, and the published experimental result of FCRN in OIRDS was pointed out and the corrected result was given. All source codes and databases are available at http://www.github.com/cxy177/SDBN.
机译：由于它们的小尺寸，在各种照明和阴影下发生模糊的外观，以及树木和建筑物，卫星图像中的卫星图像中的目标检测很难。最近，通过在慕尼黑车辆数据库中的最先进的性能提出和实现了完全卷积回归网络（FCRN）。然而，这种单阶段的方法通常会在困难的地方犯错，因为它的迅速浏览并拒绝任何第二次检查。在这封信中，设计了一种新的对象空间密度建设网（SDBN），提出了一种两相检测方法。它使用第一个SDBN来生成候选区域和第二个SDBN，继续对象类别进行一丝不苟。在四个机动目标数据库，慕尼黑车辆数据库，San-Francisco（OVDS）的开放式车辆数据库，开销图像数据集（OAD）和开放式飞机数据库（OAD）的实验表明，所提出的方法优于FCRN一个明显的余量。另外，基于空间密度图计算所有对象的精确的几何参数（位置，取向和长度），并指出了oIrds中的Fcrn的已发布的实验结果，并给出了校正的结果。所有源代码和数据库都可以在http://www.github.com/cxy177/sdbn中获得。

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