A novel benchmark dataset of color steel sheds for remote sensing image retrieval

Benchmark datasets play an important role in evaluating remote sensing image retrieval (RSIR) methods. The current datasets cover many scene categories, but omit an important scene of color steel sheds, which are widely distributed with a large number on the earth’s surface. Therefore, we propose a new benchmark dataset of color steel sheds (CSS) from Google map imagery for RSIR and share it open access in our V-RSIR system. The new dataset has rich intra-class and inter-class diversity, and is composed of blue, red and white color steel sheds with the total number of 2407 remote sensing images. We conduct evaluation experiments on the new dataset by using ten low/mid feature-based and ten deep learning feature-based methods. Experimental results indicate that the dataset is effective for evaluating RSIR methods and using the dataset can construct an effective retrieval model for color steel sheds. Besides, we have experimentally demonstrated that color constancy does affect retrieval performance on our CSS dataset. Furthermore, some experiments of merging the CSS dataset with the PatternNet, VGoogle and NWPU45 datasets are also conducted. Experimental results demonstrate that our dataset can be used as a complement to other retrieval datasets. Furthermore, these experimental results can be used as baseline for future applications on RSIR.