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

Cheng, Gong; Han, Junwei; Lu, Xiaoqiang

doi:10.1109/jproc.2017.2675998

Cited by 2,020 publications

(1,291 citation statements)

References 170 publications

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“…log Pr(ye n j = 0|X; W, w encode ), (2) where β (edge) = f requency(edge) f requency(edge)+ f requency(non−edge)…”

Section: Joint Semantic Loss and Edge Lossmentioning

confidence: 99%

“…With the rapid development of remote sensing, it has become much easier to obtain high-resolution images [1][2][3]. The ever-increasing amount of data places more emphasis on automated interpretation of remote sensing images [2,4].…”

Section: Introductionmentioning

confidence: 99%

“…The ever-increasing amount of data places more emphasis on automated interpretation of remote sensing images [2,4]. As an important step towards scene understanding [5], segmentation plays a vital role in many important remote sensing applications [6], such as natural hazards detection [7], urban planning [8,9], land cover mapping [10] and so on.…”

Section: Introductionmentioning

confidence: 99%

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ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

et al. 2018

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Abstract:The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

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“…log Pr(ye n j = 0|X; W, w encode ), (2) where β (edge) = f requency(edge) f requency(edge)+ f requency(non−edge)…”

Section: Joint Semantic Loss and Edge Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

et al. 2018

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“…As an emerging field, hyperspectral remote sensing has been introduced in a wide range of scenes increasingly. Even for hyperspectral image (HSI)-based classification and target detection, the technology has been successfully applied in aerospace, agriculture, forestry, mineral, atmospheric sciences, military and so on [2] [3]. Apart from these conventional applications, HSI also has great potential in health and pharmaceutical areas for its nature of non-intrusive inspection [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Spatial‐spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling

Qing

Ruan

et al. 2019

IET Image Processing

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This version is available at https://strathprints.strath.ac.uk/65510/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract: For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random fields (MRF). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilized as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic (MLL) prior for regularizing the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation (LBP). In comparison with several state-of-the-art approaches for data classification on 3 publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology. Keywords Hyperspectral image (HSI); spatial-spectral classification; convolutional neural networks (CNN); Markov random fields (MRF); loopy belief propagation (LBP). IntroductionHyperspectral remote sensing, a technology of acquiring remote sensing image in high-resolution spectrum, is capable of simultaneously collecting spectral and spatial information for earth observation, especially land cover analysis [1]. As an emerging field, hyperspectral remote sensing has been introduced in a wide range of scenes increasingly. Even for hyperspectral image (HSI)-based classification and target detection, the technology has been successfully applied in aerospace, agriculture, forestry, mineral, atmospheric sciences, military and so on [2] [3]. Apart from these conventional applicati...

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“…Finally, functional zones can be labeled with categories based on their features. Previous efforts at functional-zone analysis focus mainly on feature representations [9][10][11][12][13][14] and classification methods [4,15,16], but ignore zone segmentation. This is unfortunate because zone segmentation is an essential precursor to the other two steps of functional-zone analysis and is hence fundamental to the entire undertaking.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities-Beijing, Putian, and Zhuhai-and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

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

Cited by 2,020 publications

References 170 publications

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

Spatial‐spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

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