级联卷积神经网络的遥感影像飞机目标检测

传统遥感影像飞机目标检测算法依赖于人工设计特征,对大范围复杂场景和多尺度的飞机目标稳健性较差,基于深层卷积神经网络的目标检测算法通常难以有效应对大幅影像的目标搜索和弱小目标检测问题,针对上述问题,本文提出了一种基于级联卷积神经网络的遥感影像飞机目标检测算法。首先根据全卷积神经网络能够支持输入任意大小图像的特点,采用小尺度浅层全卷积神经网络对整幅影像进行遍历和搜索,快速获取疑似飞机目标作为兴趣区域,然后利用较深层的卷积神经网络对兴趣区域进行更精确的目标分类与定位。为提高卷积神经网络对地物目标的辨识能力,在卷积层中引入多层感知器,并在训练过程中采取多任务学习与离线难分样本挖掘的策略;在测试阶段,建立影像金字塔进行多级搜索,并结合非极大值抑制消除冗余窗口,从而实现由粗到精的飞机目标检测与识别。对多个数据集下多种复杂场景的遥感影像进行测试,结果表明,本文方法具有较高的准确性和较强的稳健性,可为大幅遥感影像的飞机目标检测问题提供一个快速高效的解决方案。

Aircraft detection in remote sensing images using cascade convolutional neural networks

Traditional aircraft detection algorithms which adopt handcraft features have poor performance in complex scene images and recognizing multi-scale objects. Methods using deep convolutional neural networks still face difficulty in dim small target search and recognition in large images with complex background. Aiming at these problems, a coarse-to-fine algorithm for aircraft detection in remote sensing images using cascade convolutional neural networks is proposed. To quickly and effectively acquire suspicious regions of interest (ROI), the whole image is searched by a small and shallow fully convolutional neural network which could deal with images of any size. Then deeper convolutional neural networks are used to refine the classification and location of the ROIs. A multilayer perceptron is introduced to the convolutional layer to improve identification capability of the convolutional neural networks and the strategies of multi-task learning and offline hard example mining are adopted in the process of training. At the detecting stage, the image pyramid is constructed and the redundant windows could be eliminated by the non-maximal suppression. Multiple datasets are tested and the results show that the proposed method has higher accuracy and stronger robustness and provides a fast and efficient solution for object detection in large remote sensing images.