基于多尺度形变特征卷积网络的高分辨率遥感影像目标检测

传统的基于滑窗搜索和人工设计特征相结合的目标检测方法难以适用于海量高分辨率遥感图像的目标检测任务。本文提出了一种基于多尺度形变特征卷积网络的目标检测方法，利用可形变卷积网络对具有尺度和方向变化的遥感图像目标进行特征提取，然后对多层残差模块提取出的形变特征进行区域预测和鉴别。具体模型包括两个子网络：①目标区域预测子网络用于从多层深度特征图提取目标候选区域；②目标区域鉴别子网络用于对目标候选区域进行分类和位置回归。本文在光学卫星图像10类目标数据集上对比了多种基于深度学习的目标检测算法，并将训练好的模型用于谷歌地球影像飞机坟场数据集和高分2号、吉林1号数据集的评估，试验结果表明本文方法能够快速准确地对多类目标进行检测，具有较好的稳健性和迁移性。

Object Detection in Remote Sensing Imagery with Multi-scale Deformable Convolutional Networks

Traditional target detection methods based on sliding window search paradigm and hand-craft based features are difficult to be applied to the multi-class target detection of very-high-resolution remote sensing images. In this paper,we proposed a deformable convolutional networks based multi-class target detection method by introducing deformable convolution layer and deformable RoI (Region-of-Interest) pooling layer. Specially,our method consists of two sub networks:a region proposal network aims to predict candidate regions from several layers with different filter size,and a region classification network for discrimination and regression. The quantitative comparison results on the challenging NWPU VHR-10 data set,large-scale Google Earth images, GF-2 and JL-1 images show that our method is more accurate and robust than existing algorithms.