PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval

By Weixun Zhou, Shawn D. Newsam, Congmin Li, Zhenfeng Shao. 2017

561 citations recorded by the source corpus.

Abstract Benchmark datasets are critical for developing, evaluating, and comparing remote sensing image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in that (1) they were originally collected for land use/land cover classification instead of RSIR; (2) they are relatively small in terms of the number of classes as well as the number of images per class which makes them unsuitable for developing deep learning based approaches; and (3) they are not appropriate for RSIR due to the large amount of background present in the images. These limitations restrict the development of novel approaches for RSIR, particularly those based on deep learning which require large amounts of training data. We therefore present a new large-scale remote sensing dataset termed “PatternNet” that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. Significantly, PatternNet’s large scale makes it suitable for developing novel, deep learning based approaches for RSIR. We use PatternNet to evaluate the performance of over 35 RSIR methods ranging from traditional handcrafted feature based methods to recent, deep learning based ones. These results serve as a baseline for future research on RSIR.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Evaluation & Benchmarks

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Citations in the corpus

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (2017, background): “create novel datasets, [22], [27], [28], [30] such as the two benchmark datasets PatternNet [37] and NWPU-RESISC45 [7].”
CILEA-NET: Curriculum-Based Incremental Learning Framework for Remote Sensing Image Classification (2021, background): “From the results presented in Table I, it is apparent that our approach outperforms the existing algorithms by a margin of 19% and about 31% for the NWPU-RESISC45 and PatternNet datasets, respectively, and by 37% for the Indian Pines dataset.”
The color out of space: learning self-supervised representations for Earth Observation imagery (2020, methodology): “In-deed, existing datasets (as Eurosat [35], PatterNet [36], UC Merced Land Use Dataset [37]) include a small number of annotated images, hence resulting inadequate for training very deep networks.”
Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding (2019, background): “Most of the benchmark archives in RS (UC Merced Land Use Dataset [2], WHU-RS19 [3], RSSCN7 [4], SIRI-WHU [5], AID [6], NWPU-RESISC45 [7], RSI-CB [8], EuroSat [9] and PatternNet [10]) contain a small number of images annotated with single category labels.”
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing (2023, unknown): “We used a total of 12 downstream datasets, including PatternNet [97], EuroSAT [98], OPTIMAL-31 [99], RSC11 [100], AID [101], MLRSNet [102], RSI-CB128 [103], RSI-CB256 [103], RESISC45 [104], WHU-earth [105], WHU-RS19 [106], and RS2800 [107].”