AID++: An Updated Version of AID on Scene Classification

Pu, Jun; Xia, Gui-Song; Hu, Fan; Lu, Qikai; Zhang, Liangpei

doi:10.1109/igarss.2018.8518882

Cited by 19 publications

(16 citation statements)

References 13 publications

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“…Without loss of generality, the overall accuracy (OA) [65], [66] and a confusion matrix [27], [28] were applied to evaluate the performance of the available benchmarking algorithms.…”

Section: B Implementation Details and Evaluation Metricsmentioning

confidence: 99%

“…There are already large-scale datasets, having been compiled in the optical remote sensing field to satisfy different requirements. The existing literatures include the UC Merced land use dataset (UC-Merced for short) [25], the local climate zone dataset [26], the aerial image dataset (AID) [27], AID++ [28], the dataset for object detection in aerial images [29], and the EuroSAT dataset [30]. Because of the clear visual appearance of optical images, any dataset compilation is relatively easy to perform.…”

Section: Introductionmentioning

confidence: 99%

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OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

Zhao

Zhang

Yao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Sentinel-1 mission provides a freely accessible opportunity for urban image interpretation based on synthetic aperture radar (SAR) data with a specific resolution, which is of paramount importance for Earth observation. In parallel, with the rapid development of advanced technologies, especially deep learning, we urgently need a large-scale SAR dataset supporting urban image interpretation. This article presents OpenSARUrban: a Sentinel-1 dataset dedicated to the content-related interpretation of urban SAR images, including a well-defined hierarchical annotation scheme, data collection, well-established procedures for dataset compilation and organization as well as properties, visualizations, and applications of this dataset. Particularly, our OpenSARUrban collection provides 33 358 image patches of urban SAR scenes, covering 21 major cities of China, including 10 different target area categories, 4 kinds of data formats, 2 kinds of polarization modes, and owning 5 essential properties: largescale coverage, diversity, specificity, reliability, and sustainability. These properties guarantee the achievement of several goals for OpenSARUrban. The first one is to support urban target characterization. The second one is to help develop well-applicable and advanced algorithms for Sentinel-1 urban target classification. The third one is to explore content-based image retrieval for these kinds of data. In addition, dataset visualization is implemented from the perspective of manifolds to give an intuitive understanding. Besides a detailed description and visualization of the dataset, we present results of some benchmarking algorithms, demonstrating that this dataset is practical and challenging. Notably, developing algorithms to enhance the classification performance on the whole dataset and considering the data imbalance are especially demanding.

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“…Without loss of generality, the overall accuracy (OA) [65], [66] and a confusion matrix [27], [28] were applied to evaluate the performance of the available benchmarking algorithms.…”

Section: B Implementation Details and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

Zhao

Zhang

Yao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…To meet the requirements of the deep learning model regarding the diversity of the samples in the dataset, the CLRS ensures the diversity and representativeness of the samples in the collection. In the same way as most of the existing datasets images are collected, such as AID++ [23], RSD46-WHU [32,33], etc., CLRS images are mainly collected from Google Earth, Bing Map, Google Map, and Tianditu, which use different remote imaging sensors. Therefore, the CLRS images are multisource and provide rich sample data.…”

Section: The Proposed Clrs Datasetmentioning

confidence: 99%

“…(1) Regarding the selection of the CLRS categories, we have referenced various land-use classification standards. The authors in Reference [23] constructed a scene category network for remote sensing image scene classification (as shown in Table 1), which synthesizes various land-use classification standards, and details which subclasses are included under each parent class. From this scene category network, we select 25 common scenarios as the scene categories of the CLRS.…”

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confidence: 99%

CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification

Jiang

et al. 2020

Sensors

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Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms based on deep learning face the problem of catastrophic forgetting, that is, they cannot maintain the performance of the old batch data. Therefore, it has become more and more important to ensure that the scene classification model has the ability of continual learning, that is, to learn new batch data without forgetting the performance of the old batch data. However, the existing remote sensing image scene classification datasets all use static benchmarks and lack the standard to divide the datasets into a number of sequential learning training batches, which largely limits the development of continual learning in remote sensing image scene classification. First, this study gives the criteria for training batches that have been partitioned into three continual learning scenarios, and proposes a large-scale remote sensing image scene classification database called the Continual Learning Benchmark for Remote Sensing (CLRS). The goal of CLRS is to help develop state-of-the-art continual learning algorithms in the field of remote sensing image scene classification. In addition, in this paper, a new method of constructing a large-scale remote sensing image classification database based on the target detection pretrained model is proposed, which can effectively reduce manual annotations. Finally, several mainstream continual learning methods are tested and analyzed under three continual learning scenarios, and the results can be used as a baseline for future work.

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“…In recent years, many efforts ( Zhu et al, 2017 ), e.g., developing novel network architectures ( Murray et al, 2019 , Cheng et al, 2020 , Bi et al, 2020 , Niazmardi et al, 2017 , Lin et al, 2020 , Zhu et al, 2018 ) and pipelines ( Byju et al, 2000 , Xu et al, 2020 , Wang et al, 2019 , Zhu et al, 2019 ), publishing large-scale datasets ( Xia et al, 2017 , Jin et al, 2018 ), introducing multi-modal and multi-temporal data ( Hu et al, 2020 , Tuia et al, 2016 , Ru et al, 2020 , Li et al, 2020a ), have been deployed to address this task, and most of them treat it as a single-label classification problem. A common assumption shared by these researches is that an aerial image belongs to only one scene category, while in real-world scenarios, it is more often that there exist various scenes in a single image (cf.…”

Section: Introductionmentioning

confidence: 99%

Aerial scene understanding in the wild: Multi-scene recognition via prototype-based memory networks

Hua

Mou

Lin

et al. 2021

ISPRS Journal of Photogrammetry and Remote Sensing

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Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we propose to take a step forward to a more practical and challenging task, namely multi-scene recognition in single images. Moreover, we note that manually yielding annotations for such a task is extraordinarily time- and labor-consuming. To address this, we propose a prototype-based memory network to recognize multiple scenes in a single image by leveraging massive well-annotated single-scene images. The proposed network consists of three key components: 1) a prototype learning module, 2) a prototype-inhabiting external memory, and 3) a multi-head attention-based memory retrieval module. To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory. Afterwards, a multi-head attention-based memory retrieval module is devised to retrieve scene prototypes relevant to query multi-scene images for final predictions. Notably, only a limited number of annotated multi-scene images are needed in the training phase. To facilitate the progress of aerial scene recognition, we produce a new multi-scene aerial image (MAI) dataset. Experimental results on variant dataset configurations demonstrate the effectiveness of our network. Our dataset and codes are publicly available 1 .

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AID++: An Updated Version of AID on Scene Classification

Cited by 19 publications

References 13 publications

OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification

Aerial scene understanding in the wild: Multi-scene recognition via prototype-based memory networks

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