论文标题
高质量的遥感图像超分辨率使用深内存连接的网络
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network
论文作者
论文摘要
单图像超分辨率是增强遥感图像空间分辨率的有效方法，这对于许多应用程序（例如目标检测和图像分类）至关重要。但是，基于神经网络的现有方法通常具有较小的接收场，而忽略了图像细节。我们提出了一种基于卷积神经网络的新型方法，名为Deep Memory Connected网络（DMCN），以重建高质量的超分辨率图像。我们构建本地和全局内存连接，以将图像细节与环境信息结合在一起。为了进一步减少参数并缓解耗时的时间，我们提出了缩小采样单元，从而缩小了特征地图的空间尺寸。我们在具有不同空间分辨率的三个遥感数据集上测试DMCN。实验结果表明，我们的方法在当前最新技术方面的准确性和视觉性能都可以提高。
Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification. However, existing methods based on the neural network usually have small receptive fields and ignore the image detail. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images. We build local and global memory connections to combine image detail with environmental information. To further reduce parameters and ease time-consuming, we propose downsampling units, shrinking the spatial size of feature maps. We test DMCN on three remote sensing datasets with different spatial resolution. Experimental results indicate that our method yields promising improvements in both accuracy and visual performance over the current state-of-the-art.
