基于多输入密集连接神经网络的遥感图像时空融合算法

为了解决地表反射率遥感卫星Landsat和MODIS影像的时空融合问题,文中提出基于多输入密集连接网络的遥感图像时空融合算法.首先提出多输入的密集连接网络,学习包含连续时刻间差异信息的过渡遥感影像.基于差异相似假设,融合网络学习得到的2幅过渡影像与已知的2幅高空间分辨率影像,得到最终的预测影像.对Landsat遥感影像和MODIS遥感影像的融合实验表明,文中算法在各项定量指标中均较优,最终的预测图像也可表明,文中算法对噪声具有较好的鲁棒性,能较好地恢复细节信息.

Spatial-Temporal Fusion Algorithm for Remote Sensing Images Based on Multi-input Dense Connected Neural Network

To solve the spatial-temporal fusion problem of images of surface reflectivity remote sensing satellites Landsat and MODIS, a spatial-temporal fusion algorithm for remote sensing images based on multi-input dense connected neural network is proposed. Firstly, a multi-input dense connected neural network is put forward to study the remote sensing images containing the difference information between continuous moments. Then, two transition images learned from the network are fused with the two known high spatial resolution images based on the difference similarity hypothesis to obtain the final predicted images. According to the fusion experiment of Landsat remote sensing images and MODIS remote sensing images, the proposed algorithm produces promising results in each quantitative index. The final predicted image by the proposed algorithm is more robust to noise with better recovered detail information.