A benchmark for scene classification of high spatial resolution remote sensing imagery

Scene classification for high-resolution remotely sensed imagery have been widely investigated in recent years. However, there is few public, widely accepted and large scale dataset for benchmarking different methods. This paper presents a new and large dataset consisting of 5000 high-resolution remote sensing images which is manually labeled in 20 semantic classes for scene classification. Each class includes more than 200 image samples with different appearances. Some classic classification algorithms are compared on this dataset. To our knowledge, this work is the first time to give a public benchmark dataset at this size on the problem of scene classification in high-resolution remote sensing imagery, and give comparative results and analysis of various classic classification algorithms.