%0 Dataset
%T MLRSNet: A Multi Label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/0165a0f2-6ded-41f4-a94b-6d0aceb3d199
%W NCDC
%R 10.17632/7j9bv9vwsx.2
%A Wang Yuebin
%K Multi label image dataset;semantic scene understanding;convolutional neural network (CNN);image classification;image retrieval
%X This dataset is a new large-scale high-resolution multi label remote sensing dataset developed by "MLRSNet" for semantic scene understanding. This dataset contains 109161 high-resolution remote sensing images labeled as 46 categories, with sample images ranging from 1500 to 3000 for each category. These images have a fixed size of 256 × 256 pixels and different pixel resolutions. In addition, each image in the dataset is labeled with multiple of the 60 predefined category labels, with each image associated with a label count ranging from 1 to 13. In addition, the construction process of the MLRSNet dataset was elaborated in detail, and the performance of various multi label based deep learning methods in image classification and image retrieval tasks was evaluated. The experimental results show that multi label based deep learning methods can achieve better performance in image classification and image retrieval tasks. This dataset is currently the largest high-resolution multi label remote sensing dataset, containing the richest multi label information. And this dataset has high intra class diversity, which can provide better data resources for the evaluation and development of numerous methods in the field of semantic scene understanding.