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Two novel benchmark datasets from ArcGIS and bing world imagery for remote sensing image retrieval

Dongyang Hou,Z. Miao,H. Xing,Hao Wu

Published 2021 in International Journal of Remote Sensing

ABSTRACT

ABSTRACT Benchmark datasets are essential to develop and evaluate remote sensing image retrieval (RSIR) approaches. However, there are no two datasets with different remote sensing image sources, an approximate equal number of images, the same classification system, and image size in the existing public benchmark datasets. This may affect the evaluation of the universality and robustness of RSIR approaches for the same category in different datasets, and even hinder the development of new cross-source RSIR approaches that require remote sensing images from different sources. We therefore present two new large-scale datasets from ArcGIS and Bing World Imagery, respectively. Similar to the PatternNet dataset from Google Earth Imagery, both two new collected datasets contain 38 classes using the same classification system and each class has at least 1,500 images. We conduct experiments using five handcrafted low/mid-level feature methods and six deep learning high-level feature methods on the two datasets. Results show that our datasets are effective for evaluating different RSIR approaches and the results can be served as the baseline for future research. We also perform the comparison and the cross analysis compared with other large-scale datasets. Results indicate that our datasets are more inclusive, richer variations and better intra-class diversity. Besides, other experimental results show that our new datasets and the VGoogle (extracted by volunteers from Google imagery) dataset can be merged into one dataset for larger-scale remote sensing image retrieval.

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