This paper presents a new simplex-based method for unsupervised endmember extraction, called maximum abundance sum-to-one constraint (ASC) fraction (MAF). The ASC fractions refer to the spectral unmixing results with ...
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This paper presents a new simplex-based method for unsupervised endmember extraction, called maximum abundance sum-to-one constraint (ASC) fraction (MAF). The ASC fractions refer to the spectral unmixing results with the abundance sum-to-one constraint unmixing only. The algorithm assumes the existence of the pure pixels in the input data for every endmember in the scene, and exploits the fact that pixels with maximum ASC fractions are corresponding to the vertices of a simplex. In order to demonstrate the performance of the proposed MAF, the N-findr algorithm (N-FINDR) and vertex component analysis (VCA) based merely on PCA dimensional reduction are used for comparison. Experiments using both simulated and real hyperspectral data show that MAF is effective in searching optimal results, with a low computational complexity.
It is difficult to segment instances of object classes accurately unsupervised in images, because of the complexity of structures, inter-class differences, background interference and so on. A multi-scale semantic mod...
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The objective of this work is multiple objects detection in remote sensing images. Many classifiers have been proposed to detect military objects. In this paper, we demonstrate that linear combination of kernels can g...
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This paper proposes a new method for automatic ship targets detection in remote sensing images. The method uses adaptive segmentation algorithm for getting possible ship targets first, and then calculates Histograms o...
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A new empirical topographic correction method is proposed in this paper. The main idea of the new method is smoothing the slope angle of terrain in the first place and then performing the cosine correction based on th...
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A new empirical topographic correction method is proposed in this paper. The main idea of the new method is smoothing the slope angle of terrain in the first place and then performing the cosine correction based on the smoothed terrain. A comparison is conducted among the new method and several other common methods using Landsat-7 ETM+ data. Visual analysis and statistical analysis are adopted to assess the performance of these methods from two aspects: overcorrection, homogeneity within a land cover class. Comparison results indicate that the new method is superior to the cosine correction, Gamma correction, Sun-Canopy-Sensor correction, and Minnaert correction. Compared with common methods, the proposed one can eliminate overcorrection better and is an effective topographic correction method.
The ambiguous Doppler centroid causes incorrect estimation result of the radial velocity. For moving targets with fast radial velocity, an unambiguous estimation approach of the radial velocity is introduced for the a...
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To conserve and manage wetland resources, it is important to map wetlands and monitor their changes. However, wetland mapping is difficult because of spectral confusion with other landcover classes and spectral variab...
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Automatic inshore ship detection from remote sensing imagery has many important applications, such as ship change detection and harbor dynamic surveillance. Stable performance of inshore ship detection is vital to the...
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Multi-object tracking is important in many computer vision applications. The major difficulties are due to inter-object or scene occlusion and data association. In this paper, we present a method to automatically dete...
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The main goal of data fusion in remote sensing is integrating the respective superiority and complementary information of multi-source data to serve the applications. Most conventional fusion algorithm is oriented to ...
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