论文标题
NFANET：一种从高分辨率遥感图像中提取弱监督水的新方法
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
论文作者
论文摘要
深度学习进行水进行抽水需要精确的像素级标签。但是，很难在像素级别标记高分辨率遥感图像。因此，我们研究了如何利用点标签提取水体，并提出了一种称为邻居特征聚合网络（NFANET）的新方法。与Pixellevel标签相比，点标签更容易获得，但是它们会丢失很多信息。在本文中，我们利用了当地水体的相邻像素之间的相似性，并提出了一个邻居采样器来重新取消遥感图像。然后，将采样的图像发送到网络以进行特征聚合。此外，我们使用改进的递归训练算法来进一步提高提取精度，从而使水边界更加自然。此外，我们的方法利用相邻的功能而不是全球或本地功能来学习更多代表性功能。实验结果表明，所提出的NFANET方法不仅要优于其他研究的弱监督方法，而且还获得了与最先进的结果相似的结果。
The use of deep learning for water extraction requires precise pixel-level labels. However, it is very difficult to label high-resolution remote sensing images at the pixel level. Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet). Compared with pixellevel labels, point labels are much easier to obtain, but they will lose much information. In this paper, we take advantage of the similarity between the adjacent pixels of a local water-body, and propose a neighbor sampler to resample remote sensing images. Then, the sampled images are sent to the network for feature aggregation. In addition, we use an improved recursive training algorithm to further improve the extraction accuracy, making the water boundary more natural. Furthermore, our method utilizes neighboring features instead of global or local features to learn more representative features. The experimental results show that the proposed NFANet method not only outperforms other studied weakly supervised approaches, but also obtains similar results as the state-of-the-art ones.
