Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery

Zhu, Qiqi; Zhong, Yanfei; Zhao, Binghai; Xia, Gui-Song; Zhang, Liangpei

doi:10.1109/lgrs.2015.2513443

Cited by 325 publications

(160 citation statements)

References 22 publications

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“…Whereas, SIFT descriptor and HOG feature are local features that are used for the representations of local structure [108] and shape information [109]. To represent an entire scene image, they are generally used as building blocks to construct global image features, such as the well-known bag-of-visual-words (BoVW) models [6,8,9,14,19,29,36,38,39,55,93,101,122,123] and HOG feature-based part models [22,23,27,103]. In addition, a number of improved feature encoding/pooling methods have also been proposed in the past few years, such as Fisher vector coding [10,14,84,86], spatial pyramid matching (SPM) [124], and probabilistic topic model (PTM) [11,40,42,43,92,123], etc.…”

Section: A Handcrafted Feature Based Methodsmentioning

confidence: 99%

“…Every individual cue captures only one aspect of the scene, so one single type of feature is always inadequate to represent the content of the entire scene image. Accordingly, a combination of multiple complementary features for scene classification [8,9,11,12,20,30,33,85,88,89,92,125] is considered as a potential strategy to improve the performance. For example, Zhao et al [11] presented a dirichlet derived multiple topic model to combine three types of features at a topic level for scene classification.…”

Section: A Handcrafted Feature Based Methodsmentioning

confidence: 99%

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Remote Sensing Image Scene Classification: Benchmark and State of the Art

2017

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This paper reviews the recent progress of remote sensing image scene classification, proposes a large-scale benchmark dataset, and evaluates a number of state-of-the-art methods using the proposed dataset.Gong Cheng, Junwei Han, Senior Member, IEEE, and Xiaoqiang Lu, Senior Member, IEEE Abstract-Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.

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Section: A Handcrafted Feature Based Methodsmentioning

confidence: 99%

Section: A Handcrafted Feature Based Methodsmentioning

confidence: 99%

Remote Sensing Image Scene Classification: Benchmark and State of the Art

2017

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“…Hence, to extract a holistic and discriminative feature representation is the most significant part for scene classification. Traditional approaches are mostly based on the Bag-of-Visual-Words (BoVW) model [78,79], but their potential for improvement was limited by the ability of experts to design the feature extractor and the expressive power encoded.…”

Section: B Interpretation Of Sar Imagesmentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a "black-box" solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization. Following this wave of success and thanks to the increased availability of data and computational resources, the use of deep learning in remote sensing is finally taking off in remote sensing as well. Remote sensing data bring some new challenges for deep learning, since satellite image analysis raises some unique questions that translate into challenging new scientific questions:• Remote sensing data are often multi-modal, e.g. from optical (multi-and hyperspectral) and synthetic aperture radar (SAR) sensors, where both the imaging geometries and the content are completely different. Data and information fusion uses these complementary

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“…We used the traditional two methods and also reproduced the method which uses BoVW model [39]. Simultaneously, we fine-tune the three famous and well-trained CNN models to make the model adaptable to the new classified data set.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification

Feng

Liu

2017

Computational Intelligence and Neuroscience

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With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images. Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories.

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Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery

Cited by 325 publications

References 22 publications

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification

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