A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data

Combined multivariate drought index for drought assessment in China from 2003 to 2020

Accurate water extraction using remote sensing imagery based on normalized difference water index and unsupervised deep learning

MSNet: multispectral semantic segmentation network for remote sensing images

LabelRS: An Automated Toolbox to Make Deep Learning Samples from Remote Sensing Images

Deep learning technology has achieved great success in the field of remote sensing processing. However, the lack of tools for making deep learning samples with remote sensing images is a problem, so researchers have to rely on a small amount of existing public data sets that may influence the learning effect. Therefore, we developed an add-in (LabelRS) based on ArcGIS to help researchers make their own deep learning samples in a simple way. In this work, we proposed a feature merging strategy that enables LabelRS to automatically adapt to both sparsely distributed and densely distributed scenarios. LabelRS solves the problem of size diversity of the targets in remote sensing images through sliding windows. We have designed and built in multiple band stretching, image resampling, and gray level transformation algorithms for LabelRS to deal with the high spectral remote sensing images. In addition, the attached geographic information helps to achieve seamless conversion between natural samples, and geographic samples. To evaluate the reliability of LabelRS, we used its three sub-tools to make semantic segmentation, object detection and image classification samples, respectively. The experimental results show that LabelRS can produce deep learning samples with remote sensing images automatically and efficiently.