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Fen Chen
School of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-tech Zone, Chengdu, Sichuan 611731, China

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Journal article
Published: 25 May 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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The Altai Mountains are one of the most impressive and valuable archaeological areas in the world. Kurgans (burial mounds) of ancient civilizations, which are scattered across the vast Altai area, are an exceptionally valuable source of information for archaeology. These precious archaeological resources, which sometimes have been preserved intact in the permafrost underground for over two millennia, are now under various threats, such as natural disasters, farmland expansion, touristic development, and most notably global warming. A detailed map or inventory of the mounds is essential but is still not available. In this study, we test the deep convolutional neural network (CNN) technique for automatic detection of stone mounds from high-resolution satellite images in four regions in the Altai Mountains. We propose three improvement techniques to increase the performance of off-the-shelf object detection methods that are originally proposed for daily-life objects. Our results demonstrate that it is feasible to apply CNN to detect stone mounds, and the detection results are good enough to capture their spatial distribution. CNN-based object detection can largely narrow down the search area for archaeologists in yet un-surveyed regions, and is therefore useful for preparing field survey campaigns and directing archaeological fieldwork. We also applied the method to an un-surveyed Altai Mountain area and successfully discovered stone mounds that are yet undocumented. Our method can potentially be applied to construct an inventory for all stone mounds present in the whole Altai Mountain region.

ACS Style

Fen Chen; Rui Zhou; Tim Van de Voorde; Xingzhuang Chen; Jean Bourgeois; Wouter Gheyle; Rudi Goossens; Jian Yang; Wenbo Xu. Automatic detection of burial mounds (kurgans) in the Altai Mountains. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 177, 217 -237.

AMA Style

Fen Chen, Rui Zhou, Tim Van de Voorde, Xingzhuang Chen, Jean Bourgeois, Wouter Gheyle, Rudi Goossens, Jian Yang, Wenbo Xu. Automatic detection of burial mounds (kurgans) in the Altai Mountains. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 177 ():217-237.

Chicago/Turabian Style

Fen Chen; Rui Zhou; Tim Van de Voorde; Xingzhuang Chen; Jean Bourgeois; Wouter Gheyle; Rudi Goossens; Jian Yang; Wenbo Xu. 2021. "Automatic detection of burial mounds (kurgans) in the Altai Mountains." ISPRS Journal of Photogrammetry and Remote Sensing 177, no. : 217-237.

Communication
Published: 28 January 2021 in Remote Sensing
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In this short communication, we describe the shortcomings and pitfalls of a commonly used method to detect ground materials that relies on setting thresholds for normalized difference indices. We analyze this method critically and present some experimental results on the USGS and ECOSTRESS spectral libraries and on real Sentinel-2 and Landsat-8 images. We demonstrate the risk of commission errors and provide some suggestions to reduce it.

ACS Style

Fen Chen; Tim Van de Voorde; Dar Roberts; Haojie Zhao; Jingbo Chen. Detection of Ground Materials Using Normalized Difference Indices with a Threshold: Risk and Ways to Improve. Remote Sensing 2021, 13, 450 .

AMA Style

Fen Chen, Tim Van de Voorde, Dar Roberts, Haojie Zhao, Jingbo Chen. Detection of Ground Materials Using Normalized Difference Indices with a Threshold: Risk and Ways to Improve. Remote Sensing. 2021; 13 (3):450.

Chicago/Turabian Style

Fen Chen; Tim Van de Voorde; Dar Roberts; Haojie Zhao; Jingbo Chen. 2021. "Detection of Ground Materials Using Normalized Difference Indices with a Threshold: Risk and Ways to Improve." Remote Sensing 13, no. 3: 450.

Journal article
Published: 24 September 2020 in ISPRS International Journal of Geo-Information
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Traditional indoor navigation algorithms generally only consider the geometrical information of indoor space. However, the environmental information and semantic parameters of a fire are also important for evacuation routing in the case of a fire. It is difficult for traditional indoor navigation algorithms to dynamically find an indoor path when a fire develops. To address this problem, we developed a multi-semantic constrained three-dimensional (3D) indoor fire evacuation routing method that considers multi-dimensional indoor fire scene-related semantics, such as path accessibility, path recognition degree, and fire parameters. Our method enhances the navigation semantics of indoor space by extending the fire-related components of indoor model based on IndoorGML and integrating location semantics of IndoorLocationGML. We also propose quantifiable indoor fire-oriented routing semantics and establish a navigation cost function that evaluates semantic changes during a fire. We designed an indoor routing algorithm with multiple semantic constraints based on the A* algorithm. The indoor routing results were analyzed and compared in simulation experiments. The experimental results showed that the proposed model can remove unusable nodes and edges from the obtained navigation path and provides a safer and more effective evacuation route than traditional algorithms.

ACS Style

Yan Zhou; Yuling Pang; Fen Chen; Yeting Zhang. Three-Dimensional Indoor Fire Evacuation Routing. ISPRS International Journal of Geo-Information 2020, 9, 558 .

AMA Style

Yan Zhou, Yuling Pang, Fen Chen, Yeting Zhang. Three-Dimensional Indoor Fire Evacuation Routing. ISPRS International Journal of Geo-Information. 2020; 9 (10):558.

Chicago/Turabian Style

Yan Zhou; Yuling Pang; Fen Chen; Yeting Zhang. 2020. "Three-Dimensional Indoor Fire Evacuation Routing." ISPRS International Journal of Geo-Information 9, no. 10: 558.

Research article
Published: 03 May 2020 in Canadian Journal of Remote Sensing
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Due to the fragmented compositional structure of urban scenes, many pixels are mixtures of multiple materials even in high spatial resolution airborne hyperspectral data. In the past ten years, sparse regression based spectral unmixing methods have achieved some noticeable results. Recently, Chen et al. proposed a jointly sparse spectral mixture analysis model for urban mapping. Their model has a high computational load, however, and wrongly detects a water component in residential areas due to the spectral confusion between water, shadow and other low-albedo land cover materials. In this paper, we propose to exclude water from the spectral mixture analysis in urban scenes. In order to decrease the computational load of Chen et al.’s approach, we propose a fast jointly sparse unmixing method. Our experiments demonstrate that the proposed method obtains a slightly degraded result but has a much lower computational load. It is fourteen times faster than their method, and only requires about one-ninth of the memory. A parallel implementation of the proposed method shows its potential to be applied in practical applications, especially in resource-constrained computational environments.

ACS Style

Fen Chen; Sijia Lu; Peng Zhao; Tim Van De Voorde; Wenbo Xu. Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method. Canadian Journal of Remote Sensing 2020, 46, 330 -343.

AMA Style

Fen Chen, Sijia Lu, Peng Zhao, Tim Van De Voorde, Wenbo Xu. Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method. Canadian Journal of Remote Sensing. 2020; 46 (3):330-343.

Chicago/Turabian Style

Fen Chen; Sijia Lu; Peng Zhao; Tim Van De Voorde; Wenbo Xu. 2020. "Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method." Canadian Journal of Remote Sensing 46, no. 3: 330-343.

Journal article
Published: 12 March 2020 in Remote Sensing of Environment
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Commonly applied water indices such as the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) were originally conceived for medium spatial resolution remote sensing images. In recent decades, high spatial resolution imagery has shown considerable potential for deriving accurate land cover maps of urban environments. Applying traditional water indices directly on this type of data, however, leads to severe misclassifications as there are many materials in urban areas that are confused with water. Furthermore, threshold parameters must generally be fine-tuned to obtain optimal results. In this paper, we propose a new open surface water detection method for urbanized areas. We suggest using inequality constraints as well as physical magnitude constraints to identify water from urban scenes. Our experimental results on spectral libraries and real high spatial resolution remote sensing images demonstrate that by using a set of suggested fixed threshold values, the proposed method outperforms or obtains comparable results with algorithms based on traditional water indices that need to be fine-tuned to obtain optimal results. When applied to the ASTER and ECOSTRESS spectral libraries, our method identified 3677 out of 3695 non-water spectra. By contrast, NDWI and MNDWI only identified 2934 and 2918 spectra. Results on three real hyperspectral images demonstrated that the proposed method successfully identified normal water bodies, meso-eutrophic water bodies, and most of the muddy water bodies in the scenes with F-measure values of 0.91, 0.94 and 0.82 for the three scenes. For surface glint and hyper-eutrophic water, our method was not as effective as could be expected. We observed that the commonly used threshold value of 0 for NDWI and MNDWI results in greater levels of confusion, with F-measures of 0.83, 0.64 and 0.64 (NDWI) and 0.77, 0.63 and 0.59 (MNDWI). The proposed method also achieves higher precision than the untuned NDWI and MNDWI with the same recall values. Next to numerical performance, the proposed method is also physically justified, easy-to implement, and computationally efficient, which suggests that it has potential to be applied in large scale water detection problem.

ACS Style

Fen Chen; Xingzhuang Chen; Tim Van de Voorde; Dar Roberts; Huajun Jiang; Wenbo Xu. Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sensing of Environment 2020, 242, 111706 .

AMA Style

Fen Chen, Xingzhuang Chen, Tim Van de Voorde, Dar Roberts, Huajun Jiang, Wenbo Xu. Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sensing of Environment. 2020; 242 ():111706.

Chicago/Turabian Style

Fen Chen; Xingzhuang Chen; Tim Van de Voorde; Dar Roberts; Huajun Jiang; Wenbo Xu. 2020. "Open water detection in urban environments using high spatial resolution remote sensing imagery." Remote Sensing of Environment 242, no. : 111706.

Journal article
Published: 22 May 2018 in International Journal of Applied Earth Observation and Geoinformation
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High spatial resolution hyperspectral imagery has shown considerable potential for deriving accurate land cover maps in urban areas. In this paper, a new classification framework for mapping land cover in urban environments using high spatial resolution hyperspectral data was proposed. The proposed classification scheme was applied to map urban land cover using APEX data in the city of Baden, Switzerland. We first used the NDWI and NDVI indices to separate the land cover in the scene into three main classes: water, vegetation and non-vegetated surface. Then we partitioned the scene into many superpixels and applied classification using a SVM separately on the vegetation and non-vegetated surfaces. Soil was classified both in vegetation and non-vegetated surface, and these two soil results were merged in the final classification map. Shadows were initially classified in shaded vegetation surfaces and shaded non-vegetated surfaces, and then they were further classified into meaningful land cover categories. Our experimental results demonstrate that the proposed classification framework is well suited for mapping land cover in urban environments using high resolution hyperspectral data. Although the proposed method performs better than traditional methods in terms of soil classification accuracy, our findings emphasize that the soil class should be interpreted with caution in urban land cover maps derived from remote sensing data, even when high spatial resolution hyperspectral data are used. Results from this study also demonstrate that although shaded surfaces are generally classified as a single category in urban environments, in high resolution hyperspectral data, the shadows can be further classified into meaningful land cover classes with an acceptable accuracy.

ACS Style

Fen Chen; Huajun Jiang; Tim Van de Voorde; Sijia Lu; Wenbo Xu; Yan Zhou. Land cover mapping in urban environments using hyperspectral APEX data: A study case in Baden, Switzerland. International Journal of Applied Earth Observation and Geoinformation 2018, 71, 70 -82.

AMA Style

Fen Chen, Huajun Jiang, Tim Van de Voorde, Sijia Lu, Wenbo Xu, Yan Zhou. Land cover mapping in urban environments using hyperspectral APEX data: A study case in Baden, Switzerland. International Journal of Applied Earth Observation and Geoinformation. 2018; 71 ():70-82.

Chicago/Turabian Style

Fen Chen; Huajun Jiang; Tim Van de Voorde; Sijia Lu; Wenbo Xu; Yan Zhou. 2018. "Land cover mapping in urban environments using hyperspectral APEX data: A study case in Baden, Switzerland." International Journal of Applied Earth Observation and Geoinformation 71, no. : 70-82.

Journal article
Published: 12 March 2018 in Remote Sensing
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Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

ACS Style

Fen Chen; Ruilong Ren; Tim Van De Voorde; Wenbo Xu; Guiyun Zhou; Yan Zhou. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks. Remote Sensing 2018, 10, 443 .

AMA Style

Fen Chen, Ruilong Ren, Tim Van De Voorde, Wenbo Xu, Guiyun Zhou, Yan Zhou. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks. Remote Sensing. 2018; 10 (3):443.

Chicago/Turabian Style

Fen Chen; Ruilong Ren; Tim Van De Voorde; Wenbo Xu; Guiyun Zhou; Yan Zhou. 2018. "Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks." Remote Sensing 10, no. 3: 443.

Journal article
Published: 01 July 2017 in Remote Sensing of Environment
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ACS Style

Fen Chen; Ke Wang; Tim Van de Voorde; Ting Feng Tang. Mapping urban land cover from high spatial resolution hyperspectral data: An approach based on simultaneously unmixing similar pixels with jointly sparse spectral mixture analysis. Remote Sensing of Environment 2017, 196, 324 -342.

AMA Style

Fen Chen, Ke Wang, Tim Van de Voorde, Ting Feng Tang. Mapping urban land cover from high spatial resolution hyperspectral data: An approach based on simultaneously unmixing similar pixels with jointly sparse spectral mixture analysis. Remote Sensing of Environment. 2017; 196 ():324-342.

Chicago/Turabian Style

Fen Chen; Ke Wang; Tim Van de Voorde; Ting Feng Tang. 2017. "Mapping urban land cover from high spatial resolution hyperspectral data: An approach based on simultaneously unmixing similar pixels with jointly sparse spectral mixture analysis." Remote Sensing of Environment 196, no. : 324-342.

Journal article
Published: 21 June 2016 in IEEE Transactions on Geoscience and Remote Sensing
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In remote sensing data exploitation, the spectral mixture analysis technique is generally used to detect the land cover materials and their corresponding proportions present in the observed scene. Traditionally, a fixed endmember spectral signature for each land cover material is used to perform the unmixing task. In the literature, some scholars have proposed performing the unmixing by taking the spectral variability into consideration. Among these spectral-variability-based unmixing approaches, multiple-endmember spectral mixture analysis (MESMA) is probably the most widely used method. However, when the number of land cover materials is large, the computational load of the MESMA method could be very heavy. In this paper, a sparse multiple-endmember spectral mixture model (SMESMM) is proposed to handle this problem. This model treats the spectral mixture procedure as a linear block sparse inverse problem. The SMESMM is first solved using a block sparse algorithm to obtain an initial block sparse solution. Then, MESMA is used to resolve the mixed pixel using the selected land cover materials, which correspond to the nonzero blocks in the solution obtained in the first step. The block sparse solution obtained in the first step can help to determine how many and which land cover materials are involved in the considered mixed pixel. This can largely decrease the number of possible candidate models for the MESMA method when the number of land cover materials is large. Experimental results on simulated and real hyperspectral data demonstrate the efficacy of the proposed method.

ACS Style

Fen Chen; Ke Wang; Ting Feng Tang. Spectral Unmixing Using a Sparse Multiple-Endmember Spectral Mixture Model. IEEE Transactions on Geoscience and Remote Sensing 2016, 54, 5846 -5861.

AMA Style

Fen Chen, Ke Wang, Ting Feng Tang. Spectral Unmixing Using a Sparse Multiple-Endmember Spectral Mixture Model. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54 (10):5846-5861.

Chicago/Turabian Style

Fen Chen; Ke Wang; Ting Feng Tang. 2016. "Spectral Unmixing Using a Sparse Multiple-Endmember Spectral Mixture Model." IEEE Transactions on Geoscience and Remote Sensing 54, no. 10: 5846-5861.

Articles
Published: 03 July 2014 in Remote Sensing Letters
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Hyperspectral unmixing is a technique that can be used to find the materials and their corresponding proportions present in observed mixed pixels. It has been widely used for hyperspectral data exploitation. Traditionally, this problem is solved using the constrained least squares optimization, which would lead to a dense solution. In recent years, researchers have observed that the number of materials in a mixed pixel is generally much smaller than the whole number of materials present in a given scene. They proposed to solve the problem through sparse unmixing approaches, which generally lead to constrained optimization. These sparse unmixing approaches have received considerable attention over the past few years. From the perspective of Bayesian inference, these methods assume fixed priors. In this article, we propose to solve the spectral unmixing problem by using sparse Bayesian learning (SBL) framework. In contrast to the approaches that use fixed priors, SBL incorporates a hierarchical parameterized prior that encourages sparsity in representation in its Bayesian inference to obtain a sparse solution. Experimental results on simulated data sets and a real hyperspectral scene demonstrate that the proposed method can achieve good performance compared with some state-of-the-art sparse unmixing algorithms. Our results also show that the proposed method can achieve good performance with a set of default parameter settings.

ACS Style

F. Chen; K. Wang; T.F. Tang. Hyperspectral image unmixing using a sparse Bayesian model. Remote Sensing Letters 2014, 5, 642 -651.

AMA Style

F. Chen, K. Wang, T.F. Tang. Hyperspectral image unmixing using a sparse Bayesian model. Remote Sensing Letters. 2014; 5 (7):642-651.

Chicago/Turabian Style

F. Chen; K. Wang; T.F. Tang. 2014. "Hyperspectral image unmixing using a sparse Bayesian model." Remote Sensing Letters 5, no. 7: 642-651.

Journal article
Published: 29 July 2011 in International Journal of Remote Sensing
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ACS Style

Fen Chen; Jiang L. Ma; Jonathan C.-W. Chan; Dong M. Yan. Quantitative measurement of the homogeneity and contrast of step edges in the estimation of the point spread function of a satellite image. International Journal of Remote Sensing 2011, 32, 7179 -7201.

AMA Style

Fen Chen, Jiang L. Ma, Jonathan C.-W. Chan, Dong M. Yan. Quantitative measurement of the homogeneity and contrast of step edges in the estimation of the point spread function of a satellite image. International Journal of Remote Sensing. 2011; 32 (22):7179-7201.

Chicago/Turabian Style

Fen Chen; Jiang L. Ma; Jonathan C.-W. Chan; Dong M. Yan. 2011. "Quantitative measurement of the homogeneity and contrast of step edges in the estimation of the point spread function of a satellite image." International Journal of Remote Sensing 32, no. 22: 7179-7201.

Conference paper
Published: 01 January 2007 in 2007 International Conference on Machine Learning and Cybernetics
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In the work [1], Michael K. NG et al introduced Neumann boundary condition (BC) for blurring models and provided fast algorithms for image deblurring. Whereas only even symmetric discrete Neumann BC was discussed in their paper. We present in this paper odd symmetric discrete Neumann BC for image deblurring and its corresponding diagonalization algorithm as a supplement to their work. Numerical results are given for comparison. Our work could illustrate that discrete approximations of each continuous problem would bring a new level of variety and complexity, often in the boundary conditions.

ACS Style

Fen Chen; Li Yu. Image Deblurring with Odd Symmetry Discrete Neumann Boundary Condition. 2007 International Conference on Machine Learning and Cybernetics 2007, 3, 1749 -1752.

AMA Style

Fen Chen, Li Yu. Image Deblurring with Odd Symmetry Discrete Neumann Boundary Condition. 2007 International Conference on Machine Learning and Cybernetics. 2007; 3 ():1749-1752.

Chicago/Turabian Style

Fen Chen; Li Yu. 2007. "Image Deblurring with Odd Symmetry Discrete Neumann Boundary Condition." 2007 International Conference on Machine Learning and Cybernetics 3, no. : 1749-1752.