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Spectral unmixing is an important technique to exploit mineral distribution through remote sensing image. In this paper, we propose an unmixing algorithm combining clustering-aware method with the sparsity-constrained nonnegative matrix factorization (SNMF) algorithm. Pixels with similar spectra have high possibility to share similar typical endmembers, therefore we preprocess the image using K-means...
This letter depicts a ship detection scheme for synthetic aperture radar images, utilizing a segmentation based global iterative censoring algorithm. In the proposed scheme, the fuzzy local information c-means clustering (RFLICM) algorithm is adopted to partition the inhomogeneous SAR image into numerous homogeneous sub-regions, thereby eliminating the performance degradation caused by SAR image inhomogeneity...
In this paper, we propose a Spark-based fuzzy local information C-Means (FLICM) algorithm that provides synthetic aperture radar (SAR) image change detection. With the volume and resolution of SAR images increasing, current serial clustering algorithms are not suitable to handle big data, scalable solutions are indispensable. The proposed algorithm based on Spark framework implements FLICM algorithm...
Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of “blocklike” structure in a dissimilarity matrix (CLODD...
Image change detection has a wide range of applications in various fields, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of algorithm rise, the demand for processing power is increasing. In this paper, we present a parallel FLICM algorithm for SAR image change detection on Intel MIC (Many Integrated Core) which...
In this article, a clustering-based band selection method is proposed to tackle the dimension reduction problem of hyperspectral data. The method is essentially based on low-rank doubly stochastic matrix decomposition, which is more stable than current low-rank approximation clustering methods. Experimental results show that the selected band subsets perform well in hyperspectral data classification...
LIDAR (Light Detection and Ranging) is an especially effective tool for acquiring geo-referenced point clouds of urban site. Accurate extraction of elevated features such as building rooftops is vitally important in various applications. However, it is still challenging to determine an accurate rooftop contour from the irregularly distributed LIDAR point clouds. In this paper an efficient LIDAR segmentation...
This paper presents a new method for polarimetric synthetic aperture radar (PolSAR) image classification. Firstly, to get a reasonable edge strength map, polarimetric information is used in edge strength calculation, and watershed algorithm is used to obtain the oversegmentation using the edge strength. Secondly, a searching table is used to determine the most suitable region to be merged. Finally,...
In statistical classification, such mixture models allow a formal approach to unsupervised clustering. Fitting mixture distributions can be handled by a wide variety of techniques. A standard method to fit finite mixture models to observed data is the Expectation-Maximization (EM) algorithm which is an iterative procedure which converges to a (local) maximum of the marginal a posteriori probability...
Image segmentation as a main applying field in parallel computing with high performance, its time complexity and real-time requirements of algorithm needs to continue to improve computer hardware technology and parallel computing algorithm. Mean Shift algorithm is relatively classical in image segmentation fields, which needs no prior knowledge in the process and is an unsupervised segmentation process,...
Spectral unmixing techniques decompose the pixels into constituent fractions in order to extract the subpixel information. This study reviews spectral unmixing techniques from a perspective different from earlier approaches in that the problem is studied from a classification as well as clustering perspective. In this research, we focus on addressing some core issues of spectral unmixing such as endmember...
In hyperspectral image processing technologies, anomaly detection is a valuable and practical way of searching small unknown targets based on spectral characteristics. For the lack of prior knowledge of targets, background modeling on hyperspectral images is the key process that affects the outcome of anomaly detection operator. In this paper, a novel method of anomaly detection based on quadratic...
Waveform decomposition is an important step in full-waveform LiDAR remote sensing. Under the Gaussian Mixture Model, the conventional parametric classification algorithm of Expectation-Maximization (EM) is among the most widely applied ones to decompose the waveforms. This paper introduces nonparametric classification methods, such as K-means and mean-shift to decompose the LiDAR waveforms. The experiments...
Unsupervised classification plays a key role in remote sensing hyperspectral image analysis. Complexities arise from the high dimensionality of hyperspectral imagery and this implies the need for dimensionality reduction as a vital preprocessing step. However, conventional dimensionality reduction techniques, such as linear and nonlinear manifold learning approaches, may fail if the hyperspectral...
With the widely application of high-resolution remote sensing images, its classification has attracted a lot of attention. Usually, some different categories share common patterns, which make these categories look similar. This makes the classification of such categories a challenging task. In this paper, we propose a novel dictionary learning based bilayer classification algorithm to solve this problem...
Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency...
In the multilayer perceptron (MLP), there was a theorem about the maximum number of separable regions (M) given the number of hidden nodes (H) in the input d-dimensional space. We propose a recurrence relation to prove the theorem using the expansion of recurrence relation instead of proof by induction. We use three-layer radial basis function net (RBF) on the well log data inversion to test the number...
Salient object detection in hyperspectral imagery has drawn people's attention in recent years. Some detection methods which focus on extending Itti's visual saliency model into spectral domain have been proposed. However, these methods are sensitive to high-contrast edges and cannot preserve boundary of salient object well. To address these shortcomings, we propose a region-based spectral gradient...
When dealing with optical images, the most common approach to unsupervised change detection is Change Vector Analysis (CVA) which computes the multispectral difference image and exploits its statistical distribution in (hyper-)spherical coordinates. The latter step usually requires assumptions on both the model of class distributions and the number of changes. However, both assumptions are seldom...
In this paper we present a growth-model based approach to the accurate estimation of stem diameter at single tree level by using high-density LiDAR data. First, we detect classes of trees characterized by different growth conditions by means of a data-driven inference process. To this end, all the environmental factors that can affect the growth of the tree (i.e., forest density and topography) are...
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