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Many land cover change detection (LCCD) approaches applied on very high resolution (VHR) remote sensing images utilize spatial information by using a regular window or strict mathematical model. However, regular shape or strict models cannot fit the various shapes and sizes of the ground targets. In this article, a novel LCCD approach without the parameter is proposed to detect land cover change with VHR remote sensing images. First, an adaptive spatial-context extraction algorithm is applied to explore contextual information around a pixel. Second, the change magnitude between pairwise pixels is quantitatively measured by computing the band-to-band distance which is defined by the pairwise adaptive regions around the corresponding pixels. Finally, after the generation of a change magnitude image (CMI), a binary threshold method called double-window flexible pace search (DFPS) is adopted to divide CMI into a binary change detection map. The performance of the proposed approach is verified by comparing it with five state-of-the-art methods with three pairs of VHR images. The comparisons demonstrated that the proposed approach achieved the improved detected results comparing with state-of-the-art LCCD methods. The code of the proposed approach is available at https://github.com/TongfeiLiu/ASEA-CD.
Zhiyong Lv; Fengjun Wang; Tongfei Liu; Xiangbin Kong; Jon Atli Benediktsson. Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleZhiyong Lv, Fengjun Wang, Tongfei Liu, Xiangbin Kong, Jon Atli Benediktsson. Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleZhiyong Lv; Fengjun Wang; Tongfei Liu; Xiangbin Kong; Jon Atli Benediktsson. 2021. "Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
This special section investigates the state-of-the-art in the analysis and processing of remotely sensed big data employing distributed computing architectures.
Jon Atli Benediktsson; Zebin Wu. Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]. Proceedings of the IEEE 2021, 109, 1278 -1281.
AMA StyleJon Atli Benediktsson, Zebin Wu. Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]. Proceedings of the IEEE. 2021; 109 (8):1278-1281.
Chicago/Turabian StyleJon Atli Benediktsson; Zebin Wu. 2021. "Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]." Proceedings of the IEEE 109, no. 8: 1278-1281.
Lv ZhiYong; Tongfei Liu; Jon Atli Benediktsson; Nicola Falco. Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review. IEEE Geoscience and Remote Sensing Magazine 2021, PP, 2 -21.
AMA StyleLv ZhiYong, Tongfei Liu, Jon Atli Benediktsson, Nicola Falco. Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review. IEEE Geoscience and Remote Sensing Magazine. 2021; PP (99):2-21.
Chicago/Turabian StyleLv ZhiYong; Tongfei Liu; Jon Atli Benediktsson; Nicola Falco. 2021. "Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review." IEEE Geoscience and Remote Sensing Magazine PP, no. 99: 2-21.
In this article, we propose a bilevel segmentation framework with metacognitive learning (BS-McL) to detect power lines with an RGB camera mounted on an unmanned aerial vehicle (UAV) platform. The proposed framework consists of two levels based on spectral and spatial techniques. In the first level, spectral classification is carried out using the McL method, which is an evolving online learning neural network architecture. Due to similarities in spectral intensities, few nonpower line pixels are grouped along with power line pixels. The nonpower line pixels are removed by spatial segmentation in the second level. The second level includes morphological operations such as geometric features (shape and density indices), which are applied to detect the power lines. The processing steps of BS-McL are illustrated using a synthetic image of size 9 x 6 pixels. Also, two datasets consisting of 64 images with varying backgrounds, different locations, and dimensions of power lines are used to demonstrate the performance of the proposed BS-McL. The obtained results for BS-McL are compared with five commonly used methods. For both datasets, the efficiency of the BS-McL for power line extraction is better than for the methods used for comparison. Furthermore, the trained knowledge from our experimental set-up (Dataset 1: suburban scene) can be transferred to another dataset that is available publicly (Dataset 2: urban and mountain scenes) if the power line spectral values are in relevance with the distribution in the training dataset. The proposed approach BS-McL is based on online learning with a self-adaptive architecture, which provides improved generalization ability.
J. Senthilnath; Abhishek Kumar; Anurag Jain; K. Harikumar; Meenakumari Thapa; S. Suresh; Gautham Anand; Jon Atli Benediktsson. BS-McL: Bilevel Segmentation Framework With Metacognitive Learning for Detection of the Power Lines in UAV Imagery. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -12.
AMA StyleJ. Senthilnath, Abhishek Kumar, Anurag Jain, K. Harikumar, Meenakumari Thapa, S. Suresh, Gautham Anand, Jon Atli Benediktsson. BS-McL: Bilevel Segmentation Framework With Metacognitive Learning for Detection of the Power Lines in UAV Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-12.
Chicago/Turabian StyleJ. Senthilnath; Abhishek Kumar; Anurag Jain; K. Harikumar; Meenakumari Thapa; S. Suresh; Gautham Anand; Jon Atli Benediktsson. 2021. "BS-McL: Bilevel Segmentation Framework With Metacognitive Learning for Detection of the Power Lines in UAV Imagery." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-12.
Dawei Wen; Xin Huang; Francesca Bovolo; Jiayi Li; Xinli Ke; Anlu Zhang; Jon Atli Benediktsson. Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine 2021, PP, 2 -35.
AMA StyleDawei Wen, Xin Huang, Francesca Bovolo, Jiayi Li, Xinli Ke, Anlu Zhang, Jon Atli Benediktsson. Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine. 2021; PP (99):2-35.
Chicago/Turabian StyleDawei Wen; Xin Huang; Francesca Bovolo; Jiayi Li; Xinli Ke; Anlu Zhang; Jon Atli Benediktsson. 2021. "Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions." IEEE Geoscience and Remote Sensing Magazine PP, no. 99: 2-35.
Training samples are usually required to train a classifier for supervised classification of very high spatial resolution (VHR) remote sensing images. However, labeling samples is often a labor-intensive and time-consuming task. To solve this problem, this study integrates histogram distribution analysis, double-window flexible pace search (DFPS), and box-whisker plot (BP) techniques into an iterative algorithm to enrich training samples. The major steps of the proposed algorithm are given as follows. First, to acquire the feature distribution of a class, a histogram of each class (HOC) based on the raw classification map is generated. Second, to cover the spectral heterogeneity of an intraclass, some pixel points in each bin of HOC are selected as the coarse training sample set (CTS). Third, to further purify the CTS, DFPS, and BP techniques are adopted to exclude outlier samples and select the representative samples to signify the corresponding class. Finally, the refined training samples are used to retrain the classifier, and the preceding steps are constructed as an iterative algorithm. Experiments were performed on three real VHR remote sensing images to demonstrate the superiorities of the proposed approach in improving classification performance with respect to the maps obtained directly by the initial training set. In addition, compared with cognate state-of-the-art methods, the proposed approach achieved an approximately 2%-13% improvement in classification accuracy. Code available here:https://github.com/ImgSciGroup/IEEE-GRSL-GSEA-Code.
Zhiyong Lv; Guangfei Li; Jixing Yan; Jon Atli Benediktsson; Zhenzhen You. Training Samples Enriching Approach for Classification Improvement of VHR Remote Sensing Image. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleZhiyong Lv, Guangfei Li, Jixing Yan, Jon Atli Benediktsson, Zhenzhen You. Training Samples Enriching Approach for Classification Improvement of VHR Remote Sensing Image. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleZhiyong Lv; Guangfei Li; Jixing Yan; Jon Atli Benediktsson; Zhenzhen You. 2021. "Training Samples Enriching Approach for Classification Improvement of VHR Remote Sensing Image." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
A functional feature extraction method based on rational function approximation for hyperspectral image (HSI) classification is proposed. In digital imagery, the spectral information of a pixel can be regarded as a 1-D signal. An HSI is composed of these 1-D signals arranged in a certain spatial structure. According to the functional characteristic of hyperspectral data, 1-D signals can be approximated by a linear combination of basis functions. Thus, a joint rational basis function system (JRBFS) based on class adaptivity is here first built for an HSI by adaptive Fourier decomposition (AFD). Second, the functional representations (FRs) and corresponding reconstructed spectral curves are obtained by decomposing the original spectral information in a JRBFS. Furthermore, the functional spectral-spatial features are extracted on the basis of FRs by an edge-preserving filtering method, FR-EPFs. Finally, the functional spectral-spatial features are used for HSI classification by SVM. Experimental results for five commonly used HSI data sets demonstrate the effectiveness and advantages of the proposed method FR-EPFs.
Zhijing Ye; Tao Qian; Liming Zhang; Lei Dai; Hong Li; Jon Atli Benediktsson. Functional Feature Extraction for Hyperspectral Image Classification With Adaptive Rational Function Approximation. IEEE Transactions on Geoscience and Remote Sensing 2021, 59, 7680 -7694.
AMA StyleZhijing Ye, Tao Qian, Liming Zhang, Lei Dai, Hong Li, Jon Atli Benediktsson. Functional Feature Extraction for Hyperspectral Image Classification With Adaptive Rational Function Approximation. IEEE Transactions on Geoscience and Remote Sensing. 2021; 59 (9):7680-7694.
Chicago/Turabian StyleZhijing Ye; Tao Qian; Liming Zhang; Lei Dai; Hong Li; Jon Atli Benediktsson. 2021. "Functional Feature Extraction for Hyperspectral Image Classification With Adaptive Rational Function Approximation." IEEE Transactions on Geoscience and Remote Sensing 59, no. 9: 7680-7694.
Impact craters, which can be considered the lunar equivalent of fossils, are the most dominant lunar surface features and record the history of the Solar System. We address the problem of automatic crater detection and age estimation. From initially small numbers of recognized craters and dated craters, i.e., 7895 and 1411, respectively, we progressively identify new craters and estimate their ages with Chang’E data and stratigraphic information by transfer learning using deep neural networks. This results in the identification of 109,956 new craters, which is more than a dozen times greater than the initial number of recognized craters. The formation systems of 18,996 newly detected craters larger than 8 km are estimated. Here, a new lunar crater database for the mid- and low-latitude regions of the Moon is derived and distributed to the planetary community together with the related data analysis.
Chen Yang; Haishi Zhao; Lorenzo Bruzzone; Jon Atli Benediktsson; Yanchun Liang; Bin Liu; Xingguo Zeng; Renchu Guan; Chunlai Li; Ziyuan Ouyang. Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning. Nature Communications 2020, 11, 1 -15.
AMA StyleChen Yang, Haishi Zhao, Lorenzo Bruzzone, Jon Atli Benediktsson, Yanchun Liang, Bin Liu, Xingguo Zeng, Renchu Guan, Chunlai Li, Ziyuan Ouyang. Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning. Nature Communications. 2020; 11 (1):1-15.
Chicago/Turabian StyleChen Yang; Haishi Zhao; Lorenzo Bruzzone; Jon Atli Benediktsson; Yanchun Liang; Bin Liu; Xingguo Zeng; Renchu Guan; Chunlai Li; Ziyuan Ouyang. 2020. "Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning." Nature Communications 11, no. 1: 1-15.
Landslide inventory mapping (LIM) plays an important role in landslide susceptibility analysis. Many LIM approaches based on change detection techniques have been proposed, but with various drawbacks. For example, existing approaches have limited capability to capture the objects of varying shapes/sizes present in an area impacted by landslide. Many existing approaches are supervised and require parameter tuning. Moreover, some methods are prone to salt-and-pepper noise. To overcome these limitations, in this letter, an algorithm based on automatic adaptive region extension using very-high-resolution remote sensing images is developed. First, a simple yet effective k-means clustering method is used to generate training samples for landslide and nonlandslide classes, which refer to changed and unchanged areas, respectively. Second, an automatic adaptive region extension algorithm is developed and applied to each pixel of the postevent image, and the label of an extended region around a pixel is determined by the nearest distance between the central pixel and the changed or unchanged samples. Finally, the labels of a pixel are recorded because a pixel in different adaptive regions may be reassigned dissimilar labels, and the final label of the pixel is consistent with its maximum assigned label. To verify the performance of the proposed approach, we conducted experiments on two different landslide sites with VHR remote sensing images in Lantau Island, Hong Kong, China. Experimental results clearly demonstrate that the proposed approach has several advantages in improving the performance of LIM with VHR remote sensing images.
Lv Zhiyong; Tongfei Liu; Robert Yu Wang; Jon Atli Benediktsson; Sudipan Saha. Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleLv Zhiyong, Tongfei Liu, Robert Yu Wang, Jon Atli Benediktsson, Sudipan Saha. Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleLv Zhiyong; Tongfei Liu; Robert Yu Wang; Jon Atli Benediktsson; Sudipan Saha. 2020. "Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
The recent availability of high-resolution multiview ZY-3 satellite images, with angular information, can provide an opportunity to capture 3-D structural features for classification. In high-resolution image classification over urban areas, objects with diverse vertical structures make urban landscape more heterogeneous in 3-D space and consequently can make the classification challenging. In this article, a novel multiangle gray-level cooccurrence tensor feature is proposed based on the multiview bands of the ZY-3 imagery, namely, GLCMMA-T. The GLCMMA-T feature captures the distributions of the gray-level spatial variation under different viewing angles, which can depict the 3-D textures and structures of urban objects. The spectral and GLCMMA-T tensor features are interpreted by two 3-D convolutional neural network (CNN) streams and then concatenated as the input to the fully connected layer. This novel multispectral and multiangle 3-D convolutional neural network (M²-3-DCNN) combines the spectral and angular information, and the fused feature has the potential to provide a comprehensive description of urban objects with complex vertical structures. The experimental results on ZY-3 multiview images from four test areas indicate that the proposed method can significantly improve the classification accuracy when compared with several state-of-the-art multiangle features and deep-learning-based image classification methods.
Xin Huang; Shuang Li; Jiayi Li; Xiuping Jia; Jun Li; Xiao Xiang Zhu; Jon Atli Benediktsson. A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -20.
AMA StyleXin Huang, Shuang Li, Jiayi Li, Xiuping Jia, Jun Li, Xiao Xiang Zhu, Jon Atli Benediktsson. A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-20.
Chicago/Turabian StyleXin Huang; Shuang Li; Jiayi Li; Xiuping Jia; Jun Li; Xiao Xiang Zhu; Jon Atli Benediktsson. 2020. "A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-20.
Weiwei Song; Shutao Li; Jon Atli Benediktsson. Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 1 -12.
AMA StyleWeiwei Song, Shutao Li, Jon Atli Benediktsson. Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; ():1-12.
Chicago/Turabian StyleWeiwei Song; Shutao Li; Jon Atli Benediktsson. 2020. "Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing , no. : 1-12.
The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced in the HSI field. This article proposes an acceleration method for HSI classification that relies on scheduling metaheuristics to automatically and optimally distribute the workload of HSI applications across multiple computing resources on a cloud platform. By analyzing the procedure of a representative classification method, we first develop its distributed and parallel implementation based on the MapReduce mechanism on Apache Spark. The subtasks of the processing flow that can be processed in a distributed way are identified as divisible tasks. The optimal execution of this application on Spark is further formulated as a divisible scheduling framework that takes into account both task execution precedences and task divisibility when allocating the divisible and indivisible subtasks onto computing nodes. The formulated scheduling framework is an optimization procedure that searches for optimized task assignments and partition counts for divisible tasks. Two metaheuristic algorithms are developed to solve this divisible scheduling problem. The scheduling results provide an optimized solution to the automatic processing of HSI big data on clouds, improving the computational efficiency of HSI classification by exploring the parallelism during the parallel processing flow. Experimental results demonstrate that our scheduling-guided approach achieves remarkable speedups by facilitating the automatic processing of HSI classification on Spark, and is scalable to the increasing HSI data volume.
Zebin Wu; Jin Sun; Yi Zhang; Yaoqin Zhu; Jun Li; Antonio Plaza; Jon Atli Benediktsson; Zhihui Wei. Scheduling-Guided Automatic Processing of Massive Hyperspectral Image Classification on Cloud Computing Architectures. IEEE Transactions on Cybernetics 2020, 51, 3588 -3601.
AMA StyleZebin Wu, Jin Sun, Yi Zhang, Yaoqin Zhu, Jun Li, Antonio Plaza, Jon Atli Benediktsson, Zhihui Wei. Scheduling-Guided Automatic Processing of Massive Hyperspectral Image Classification on Cloud Computing Architectures. IEEE Transactions on Cybernetics. 2020; 51 (7):3588-3601.
Chicago/Turabian StyleZebin Wu; Jin Sun; Yi Zhang; Yaoqin Zhu; Jun Li; Antonio Plaza; Jon Atli Benediktsson; Zhihui Wei. 2020. "Scheduling-Guided Automatic Processing of Massive Hyperspectral Image Classification on Cloud Computing Architectures." IEEE Transactions on Cybernetics 51, no. 7: 3588-3601.
The majority of the change detection (CD) methods consider spatial information by using a regular window or strict mathematical model. Moreover, these methods use the spectra directly to measure the change magnitude between bitemporal images. To solve this problem, local histogram-based analysis (LHBA) is proposed for detecting a land cover change in this letter. This new approach aims to inhibit the pseudo change by defining the local histogram trend (LHT) in an adaptive manner instead of using spectral values to measure change magnitude directly. In the proposed approach, the spatial information around each pixel is first exploited by defining an adaptive local histogram. The LHT distance between the pairwise local histograms is then developed to measure the change magnitude between the pairwise pixels of bitemporal images. Finally, the change magnitude image is generated, and a binary CD is achieved by a threshold method. Experiments based on two pairs of very high-resolution remote sensing images, which refer to land use change and landslides events, demonstrate the advantages and performance of the proposed approach.
Zhiyong Lv; Tongfei Liu; Cheng Shi; Jon Atli Benediktsson. Local Histogram-Based Analysis for Detecting Land Cover Change Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1284 -1287.
AMA StyleZhiyong Lv, Tongfei Liu, Cheng Shi, Jon Atli Benediktsson. Local Histogram-Based Analysis for Detecting Land Cover Change Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (7):1284-1287.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Cheng Shi; Jon Atli Benediktsson. 2020. "Local Histogram-Based Analysis for Detecting Land Cover Change Using VHR Remote Sensing Images." IEEE Geoscience and Remote Sensing Letters 18, no. 7: 1284-1287.
Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user's accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user's accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification.
Zhiyong Lv; Guangfei Li; Zhenong Jin; Jon Atli Benediktsson; Giles M. Foody. Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 139 -150.
AMA StyleZhiyong Lv, Guangfei Li, Zhenong Jin, Jon Atli Benediktsson, Giles M. Foody. Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (1):139-150.
Chicago/Turabian StyleZhiyong Lv; Guangfei Li; Zhenong Jin; Jon Atli Benediktsson; Giles M. Foody. 2020. "Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery." IEEE Transactions on Geoscience and Remote Sensing 59, no. 1: 139-150.
Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability (δ) in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and δ , which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).
Danfeng Liu; Liguo Wang; Jón Atli Benediktsson. An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery. Applied Sciences 2020, 10, 3581 .
AMA StyleDanfeng Liu, Liguo Wang, Jón Atli Benediktsson. An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery. Applied Sciences. 2020; 10 (10):3581.
Chicago/Turabian StyleDanfeng Liu; Liguo Wang; Jón Atli Benediktsson. 2020. "An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery." Applied Sciences 10, no. 10: 3581.
Behnood Rasti; Danfeng Hong; Renlong Hang; Pedram Ghamisi; Xudong Kang; Jocelyn Chanussot; Jon Atli Benediktsson. Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox. IEEE Geoscience and Remote Sensing Magazine 2020, 8, 60 -88.
AMA StyleBehnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson. Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox. IEEE Geoscience and Remote Sensing Magazine. 2020; 8 (4):60-88.
Chicago/Turabian StyleBehnood Rasti; Danfeng Hong; Renlong Hang; Pedram Ghamisi; Xudong Kang; Jocelyn Chanussot; Jon Atli Benediktsson. 2020. "Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox." IEEE Geoscience and Remote Sensing Magazine 8, no. 4: 60-88.
We propose an unmixing framework for enhancing endmember fraction maps using a combination of spectral and visible images. The new method, data fusion through spatial information-aided learning (DFuSIAL), is based on a learning process for the fusion of a multispectral image of low spatial resolution and a visible RGB image of high spatial resolution. Unlike commonly used methods, DFuSIAL allows for fusing data from different sensors. To achieve this objective, we apply a learning process using automatically extracted invariant points, which are assumed to have the same land cover type in both images. First, we estimate the fraction maps of a set of endmembers for the spectral image. Then, we train a spatial-features aided neural network (SFFAN) to learn the relationship between the fractions, the visible bands, and rotation-invariant spatial features for learning (RISFLs) that we extract from the RGB image. Our experiments show that the proposed DFuSIAL method obtains fraction maps with significantly enhanced spatial resolution and an average mean absolute error between 2% and 4% compared to the reference ground truth. Furthermore, it is shown that the proposed method is preferable to other examined state-of-the-art methods, especially when data is obtained from different instruments and in cases with missing-data pixels.
Fadi Kizel; Jón Atli Benediktsson. Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors. Remote Sensing 2020, 12, 1255 .
AMA StyleFadi Kizel, Jón Atli Benediktsson. Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors. Remote Sensing. 2020; 12 (8):1255.
Chicago/Turabian StyleFadi Kizel; Jón Atli Benediktsson. 2020. "Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors." Remote Sensing 12, no. 8: 1255.
This study presents a novel dual-path full convolutional network (DP-FCN) model for constructing a landslide inventory map (LIM) with bitemporal high-resolution remote sensing images. Unlike traditional methods for drawing LIM, the proposed DP-FCN directly draws LIMs from the bitemporal aerial images through a trained deep neural network without generating the change magnitude map. Thus, the proposed approach can effectively reduce the effects of pseudo changes caused by phenological differences rather than landslide events. The proposed DP-FCN model contains two modules, namely, deep feature extraction and joint feature learning networks. Deep feature extraction aims to reduce redundancy while extracting the high-level deep features from bitemporal images. Joint feature learning establishes the relationship between the deep features of bitemporal images and the ground reference map. Experiments on the real datasets of the landslide sites in Lantau Island of Hong Kong, China demonstrate the feasibility and superiority of the proposed approach in drawing LIM with very high-resolution remote sensing images. Moreover, compared with the results obtained by the state-of-the-art algorithms, the proposed DP-FCN method achieves the best performance in terms of accuracy for landslide inventory mapping.
Zhiyong Lv; Tongfei Liu; Xiangbing Kong; Cheng Shi; Jon Atli Benediktsson. Landslide Inventory Mapping With Bitemporal Aerial Remote Sensing Images Based on the Dual-Path Fully Convolutional Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4575 -4584.
AMA StyleZhiyong Lv, Tongfei Liu, Xiangbing Kong, Cheng Shi, Jon Atli Benediktsson. Landslide Inventory Mapping With Bitemporal Aerial Remote Sensing Images Based on the Dual-Path Fully Convolutional Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):4575-4584.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Xiangbing Kong; Cheng Shi; Jon Atli Benediktsson. 2020. "Landslide Inventory Mapping With Bitemporal Aerial Remote Sensing Images Based on the Dual-Path Fully Convolutional Network." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 4575-4584.
Very high-resolution (VHR) remote sensing images can geometrically depict ground targets in detail but are usually insufficient in the spectral domain. This characteristic leads to a considerable amount of noise and pseudo change in the produced binary change detection maps (BCDMs) when VHR remote sensing images are used for change detection. Here, to solve the aforementioned problem, an object-oriented key point vector distance (KPVD) is proposed to measure the change magnitude between bitemporal VHR images when land cover changes are detected. The proposed KPVD-based change detection approach comprises the following major steps. First, multiscale objects based on a postevent image are extracted by the fractional net evaluation segmentation approach, and then, the segments are taken as the unit for measuring the change magnitude between bitemporal images. Second, key points and the corresponding vector are defined to describe the object feature instead of using the total pixels within the object. Finally, KPVD is proposed to measure the change magnitude between the local areas referenced to the object in the bitemporal images. The change magnitude image (CMI) between the bitemporal images is generated while the entire images are scanned and processed object by object. A well-known automatic binary method, the Otsu approach, is employed in this article to divide CMI into a BCDM. Experimental results conducted on four real data sets demonstrate the feasibility and outperformance of the proposed KPVD-based change detection approach compared with five state-of-the-art methods in terms of visual performance and quantitative measurements.
Zhiyong Lv; Tongfei Liu; Jon Atli Benediktsson. Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 6524 -6533.
AMA StyleZhiyong Lv, Tongfei Liu, Jon Atli Benediktsson. Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (9):6524-6533.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Jon Atli Benediktsson. 2020. "Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 9: 6524-6533.
Unmanned aerial vehicle (UAV) remote sensing has a wide area of applications and in this paper, we attempt to address one such problem—road extraction from UAV-captured RGB images. The key challenge here is to solve the road extraction problem using the UAV multiple remote sensing scene datasets that are acquired with different sensors over different locations. We aim to extract the knowledge from a dataset that is available in the literature and apply this extracted knowledge on our dataset. The paper focuses on a novel method which consists of deep TEC (deep transfer learning with ensemble classifier) for road extraction using UAV imagery. The proposed deep TEC performs road extraction on UAV imagery in two stages, namely, deep transfer learning and ensemble classifier. In the first stage, with the help of deep learning methods, namely, the conditional generative adversarial network, the cycle generative adversarial network and the fully convolutional network, the model is pre-trained on the benchmark UAV road extraction dataset that is available in the literature. With this extracted knowledge (based on the pre-trained model) the road regions are then extracted on our UAV acquired images. Finally, for the road classified images, ensemble classification is carried out. In particular, the deep TEC method has an average quality of 71%, which is 10% higher than the next best standard deep learning methods. Deep TEC also shows a higher level of performance measures such as completeness, correctness and F1 score measures. Therefore, the obtained results show that the deep TEC is efficient in extracting road networks in an urban region.
J. Senthilnath; Neelanshi Varia; Akanksha Dokania; Gaotham Anand; Jón Atli Benediktsson. Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery. Remote Sensing 2020, 12, 245 .
AMA StyleJ. Senthilnath, Neelanshi Varia, Akanksha Dokania, Gaotham Anand, Jón Atli Benediktsson. Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery. Remote Sensing. 2020; 12 (2):245.
Chicago/Turabian StyleJ. Senthilnath; Neelanshi Varia; Akanksha Dokania; Gaotham Anand; Jón Atli Benediktsson. 2020. "Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery." Remote Sensing 12, no. 2: 245.