面向遥感图像的小样本目标检测改进算法研究

针对传统的遥感图像目标检测中面临的小样本以及目标样本分布不均衡等问题, 提出了一种基于改进的卷积神经网络(convolutional neural network, CNN)的遥感图像小样本目标检测算法. 首先, 该算法利用 $K$ 近邻($K$-nearest neighbor, kNN)回归分别对每个点和卷积层提取特征构建局部邻域; 同时, 通过最大池化聚合所有局部特征进行全局特征表示; 最后, 采用全连接层与缩放指数型线性单元(scaled expected linear unit, SELU)激活函数计算各类别对应的概率并分类. 实验结果表明, 该算法能够更有效地融合局部特征, 提高了遥感图像小样本目标识别与检测的精度, 同时保持信息的非局部扩散.

Improved approach to detect small sample target based on remote sensing image

In this study, an improved convolutional neural network (CNN)approach is proposed to detect small sample targets from remotesensing images. The approach is designed to address the two issuesof small target samples and the unbalanced distribution of groundobject samples with respect to target detection of small samples byremote sensors. In the proposed method, first, $K$-nearest neighbor(kNN) regression is adopted to extract the features of each pointand convolution layer to construct the local neighborhood. Second,all local features are aggregated by maximum pooling layer in CNN torepresent global features. Subsequently, the full connection layerand scaled exponential linear unit (SELU) activation function areapplied to calculate the probability corresponding to each categoryfor classification. Finally, the proposed approach is tested andevaluated on hyperspectral imager remote sensing images datasets.Experimental results show that the proposed improvements to the CNNmodel fuse fully local features and result in the effectiverecognition and detection of small sample targets from remotesensing image with high accuracy while maintaining thenonlocal diffusion capabilities of information.