ABSTRACT

Motivated by the development of deep convolution neural networks (DCNNs), the aircraft detection from remote sensing images has gained tremendous progress. However, due to complex background and multi-scale characteristics, it remains a challenge in remote sensing detection. In this paper, we propose a two-stage aircraft detection method based on deep neural networks, which integrates Deconvolution operation with Position Attention mechanism (DPANet). Specifically, considering that remote sensing images are taken from the top-down perspective, which leads to significant external structural characteristic, we introduce a deconvolution module to capture the external structural feature representation of aircraft during the feature map generation process. Moreover, aiming at reducing the error detections caused by complex background in remote sensing, we propose a position attention module in the second stage. By calculating the feature similarity between any two pixels of the target feature map, DPANet can extract the complicated internal structure feature representation of aircraft, which improve the ability to distinguish background and aircraft. By integrating the deconvolution and position attention modules, DPANet can provide better representation ability for the structural characteristic of aircraft in remote sensing images. Experimental results show that the proposed method can effectively reduce the error detections and improve the accuracy of the aircraft detection.

摘要

由于深度卷积神经网络（DCNN）的发展，从遥感图像进行飞机检测已取得了巨大的进步。然而，由于复杂的背景和多尺度特性，在遥感检测中仍然是一个挑战。在本文中，我们提出了一种基于深度神经网络的两阶段飞机检测方法，该方法将反卷积操作与位置注意机制（DPANet）集成在一起。具体来说，考虑到从上至下的角度拍摄遥感图像，这会导致明显的外部结构特征，因此我们引入了反卷积模块，以在特征图生成过程中捕获飞机的外部结构特征表示。此外，为了减少由于遥感背景复杂而导致的错误检测，我们在第二阶段提出了一个职位注意模块。通过计算目标特征图的任意两个像素之间的特征相似度，DPANet可以提取飞机的复杂内部结构特征表示，从而提高了区分背景和飞机的能力。通过集成去卷积和位置注意模块，DPANet可以在航空遥感影像中为飞机的结构特性提供更​​好的表示能力。实验结果表明，该方法可以有效减少误检，提高飞机的检测精度。DPANet可以提取飞机的复杂内部结构特征表示，从而提高了区分背景和飞机的能力。通过集成去卷积和位置注意模块，DPANet可以在航空遥感影像中为飞机的结构特性提供更​​好的表示能力。实验结果表明，该方法可以有效地减少错误检测，提高飞机检测的准确性。DPANet可以提取飞机的复杂内部结构特征表示，从而提高了区分背景和飞机的能力。通过集成去卷积和位置注意模块，DPANet可以在航空遥感影像中为飞机的结构特性提供更​​好的表示能力。实验结果表明，该方法可以有效减少误检，提高飞机的检测精度。