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  • Articles  (1,190)
  • Institute of Electrical and Electronics Engineers (IEEE)  (1,190)
  • Public Library of Science
  • 2010-2014  (1,190)
  • IEEE Geoscience and Remote Sensing Letters  (1,190)
  • 40722
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  • Articles  (1,190)
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  • 1
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    Training Sample Selection for Space-Time Adaptive Processing in Heterogeneous Environments (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: As training samples are not always identically distributed with the clutter in the cell under test (CUT) in heterogeneous environments, the estimated clutter covariance matrix for space-time adaptive processing (STAP) is not accurate, which degrades the performance of STAP. To improve the performance of STAP in heterogeneous environments, this letter proposes a novel training sample selection algorithm to estimate the covariance matrix. Based on the subaperture smoothing techniques, subapertures' covariance matrices are estimated, which are used to measure the similarities between the clutter covariance matrix of the CUT and the clutter covariance matrices of the training samples. Training samples whose clutter covariance matrices are similar to that of the CUT are selected, leading to a better estimation of the clutter covariance matrix, and the performance of STAP improves. Experimental results confirm the performance of the proposed algorithm.
    Print ISSN: 1545-598X
    Electronic ISSN: 1558-0571
    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 2
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    Weakly Supervised Learning for Target Detection in Remote Sensing Images (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: In this letter, we develop a novel framework of leveraging weakly supervised learning techniques to efficiently detect targets from remote sensing images, which enables us to reduce the tedious manual annotation for collecting training data while maintaining the detection accuracy to large extent. The proposed framework consists of a weakly supervised training procedure to yield the detectors and an effective scheme to detect targets from testing images. Comprehensive evaluations on three benchmarks which have different spatial resolutions and contain different types of targets as well as the comparisons with traditional supervised learning schemes demonstrate the efficiency and effectiveness of the proposed framework.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 3
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    Scaling Transform Method for Remotely Sensed FAPAR Based on FAPAR-P Model (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: Climate and land–atmosphere models rely on accurate land-surface parameters, such as the fraction of absorbed photosynthetically active radiation (FAPAR). It is known that FAPAR values retrieved from remote-sensing images suffer from scaling effects. Scaling transformation aims to derive accurate FAPAR values at a specific scale from values at other scales. In this letter, the scaling-effect mechanism and the scale-transformation algorithm are derived using a Taylor series expansion method based on the FAPAR model based on $P$ after simplification. The scaling algorithm was validated in the Heihe River Basin. The multiscale FAPAR values are inverted from 5-, 50-, and 100-m hyperspectral reflectance data. The scale-transformation formula was used, and the results agreed well with actual values.
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  • 4
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    Joint Sparsity Model for Multilook Hyperspectral Image Unmixing (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: Recent work on hyperspectral image (HSI) unmixing has addressed the use of overcomplete dictionaries by employing sparse models. In essence, this approach exploits the fact that HSI pixels can be associated with a small number of constituent pure materials. However, unlike traditional least-squares-based methods, sparsity-based techniques do not require a preselection of endmembers and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. In addition, this perspective has been extended so as to exploit the spatial homogeneity of abundance vectors. As a result, these techniques have been reported to provide improved estimation accuracy. In this letter, we present an alternative approach that is able to relax, yet exploit, the assumption of spatial homogeneity by introducing a model that captures both similarities and differences between neighboring abundances. In order to validate this approach, we analyze our model using simulated as well as real hyperspectral data acquired by the HyMap sensor.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 5
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    A Robust Delaunay Triangulation Matching for Multispectral/Multidate Remote Sensing Image Registration (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: A novel dual-graph-based matching method is proposed in this letter particularly for the multispectral/multidate images with low overlapping areas, similar patterns, or large transformations. First, scale invariant feature transform based matching is improved by normalizing gradient orientations and maximizing the scale ratio similarity of all corresponding points. Next, Delaunay graphs are generated for outlier removal, and the candidate outliers are selected by comparing the distinction of Delaunay graph structures. In order to bring back the inliers removed in Delaunay triangulation matching iterations and to exclude the remaining outliers, the recovery strategy equipped with the dual graph of Delaunay is explored. Inliers located in the corresponding Voronoi cells are recovered to the residual sets. The experimental results demonstrate the accuracy and robustness of the proposed algorithm for various representative remote sensing images.
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  • 6
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    Theoretical Analysis and Verification of Time Variation of Background Ionosphere on Geosynchronous SAR Imaging (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: Geosynchronous synthetic aperture radar (SAR) (GEO SAR) has the characteristic of long integration time; thus, the time-freezing model assumption of background ionosphere for traditional low Earth orbit (LEO) SAR no longer holds in GEO SAR. Furthermore, the background ionosphere variation within the integration time cannot be omitted either. In this letter, the variation of total electron content within integration time is analyzed and described in detail by using polynomial approximation, and a new GEO SAR signal model influenced by background ionosphere is also proposed. In view of this novel model, the analytical expression of image shift and defocusing phase error are derived in the first place. Then, a quantitative analysis for the image shift and image defocusing in the range and azimuth directions is conducted, and the performance bounds of time-varying parameters of background ionosphere effects on focusing are obtained. Finally, the U.S. Total Electron Content measured data are used to verify the theoretical results of background ionosphere effects on GEO SAR focusing.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 7
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    Automatic Data Quality Control of Observations in Wireless Sensor Network (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: Emerging multisource earth observation technologies represented by wireless sensor network (WSN) technology are widely used in land surface observation and simulation studies. Consequently, data quality control of massive observation data has brought challenges to researchers. This letter describes a comprehensive approach applied to automatic data quality control of WSN data. First, summarize the quality element of WSN observation data which can be achieved through automated methods by analyzing the characteristics of WSN observation data, and develop a decision algorithm for each quality element. Then, associate the data type and algorithm through data quality control rules. Finally, establish an automatic data quality control system based on data quality control rules. As a matter of fact, this system has run for one and a half years and processed more than 500 million observation data records without human intervention. Application results show that this method system can effectively control the data quality of WSN data automatically.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 8
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    An Adaptive Density-Based Model for Extracting Surface Returns From Photon-Counting Laser Altimeter Data (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission of the National Aeronautics and Space Administration is scheduled to launch in 2017. This upcoming mission aims to provide data to determine the temporal and spatial changes of ice sheet elevation, sea ice freeboard, and vegetation canopy height. A photon-counting lidar onboard ICESat-2 yields point clouds resulting from surface returns and noise. In support of the ICESat-2 mission, this letter derives an adaptive density-based model that is capable of detecting the ground surface and vegetation canopy in photon-counting laser altimeter data. Based on results from point clouds generated by a first principle simulation and those observed by the Multiple Altimeter Beam Experimental Lidar, the ground and canopy returns can be reliably extracted using the proposed approach. Further study on performance assessment shows that smoother surfaces will result in improved accuracy of ground height estimation. In addition, the proposed detection approach has better performance in environments with lower noise, although the performance evaluation metric $F$ -measure does not vary significantly over a range of noise rates (0.5–5 MHz). This proposed approach is generally applicable for surface and canopy finding from photon-counting laser altimeter data.
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  • 9
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    A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble Strategy (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: This letter addresses the problem of unsupervised land-cover classification of remotely sensed multispectral satellite images from the perspective of cluster ensembles and self-learning. The cluster ensembles combine multiple data partitions generated by different clustering algorithms into a single robust solution. A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation–maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier, although trained without any external supervision, reduces the effect of data overlapping from different clusters which otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual clustering of the ensemble.
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  • 10
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    Rotation-Invariant Object Detection in Remote Sensing Images Based on Radial-Gradient Angle (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: To improve the detection precision in complicated backgrounds, a novel rotation-invariant object detection method to detect objects in remote sensing images is proposed in this letter. First, a rotation-invariant feature called radial-gradient angle (RGA) is defined and used to find potential object pixels from the detected image blocks by combining with radial distance. Then, a principal direction voting process is proposed to gather the evidence of objects from potential object pixels. Since the RGA combined with the radial distance is discriminative and the voting process gathers the evidence of objects independently, the interference of the backgrounds is effectively reduced. Experimental results demonstrate that the proposed method outperforms other existing well-known methods (such as the shape context-based method and rotation-invariant part-based model) and achieves higher detection precision for objects with different directions and shapes in complicated background. Moreover, the antinoise performance and parameter influence are also discussed.
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