In this paper, we address a new approach for near-real-time enhancement of large-scale Geospatial and aerial remotesensing (RS) imagery that aggregates descriptive and Bayesian convex regularization paradigms for sol...
详细信息
This work presents a scale-based forward-and-backward diffusion (SFABD) scheme. The main idea of this scheme is to perform local adaptive diffusion using local scale information. To this end, we propose a diffusivity ...
详细信息
This work presents a scale-based forward-and-backward diffusion (SFABD) scheme. The main idea of this scheme is to perform local adaptive diffusion using local scale information. To this end, we propose a diffusivity function based on the Minimum Reliable Scale (MRS) of Elder and Zucker (IEEE Trans. pattern Anal. Mach. Intell. 20(7), 699-716, 1998) to detect the details of local structures. The magnitude of the diffusion coefficient at each pixel is determined by taking into account the local property of the image through the scales. A scale-based variable weight is incorporated into the diffusivity function for balancing the forward and backward diffusion. Furthermore, as numerical scheme, we propose a modification of the Perona-Malik scheme (IEEE Trans. pattern Anal. Mach. Intell. 12(7), 629-639, 1990) by incorporating edge orientations. The article describes the main principles of our method and illustrates image enhancement results on a set of standard images as well as simulated medical images, together with qualitative and quantitative comparisons with a variety of anisotropic diffusion schemes.
The proceedings contain 41 papers. The topics discussed include: orders on partial partitions and maximal partitioning of sets;connective segmentation generalized to arbitrary complete lattices;preventing chaining thr...
ISBN:
(纸本)9783642215681
The proceedings contain 41 papers. The topics discussed include: orders on partial partitions and maximal partitioning of sets;connective segmentation generalized to arbitrary complete lattices;preventing chaining through transitions while favouring it within homogeneous regions;pattern spectra from partition pyramids and hierarchies;frequent and dependent connectivities;object descriptors based on a list of rectangles: method and algorithm;primitive and grain estimation using flexible magnification for a morphological texture model;morphological bilateral filtering and spatially-variant adaptive structuring functions;advances on watershed processing on GPU architecture;fast streaming algorithm for 1-D morphological opening and closing on 2-D support;hierarchical analysis of remotesensing data: morphological attribute profiles and binary partition trees;and hierarchical segmentation of multiresolution remotesensingimages.
Linearly nonseparability and class imbalance of very high resolution (VHR) imagery make feature selection for object-oriented classification quite challenging, while such characteristics, especially class imbalance, h...
详细信息
Linearly nonseparability and class imbalance of very high resolution (VHR) imagery make feature selection for object-oriented classification quite challenging, while such characteristics, especially class imbalance, have usually been ignored in open literature. To cope with the challenges, this paper proposes a new graph-based feature selection method named locally weighted discriminating projection (LWDP). First, the popular graph-based criteria of feature selection are reformulated to present linear or nonlinear mapping in feature space. Second, weight matrices of graphs characterize dissimilarity rather than similarity between pairwise neighbors, to well-preserved local structure when the difference of distance between a sample and its neighbors is large. Finally, LWDP provides a new perspective to alleviate class imbalance at both global and local levels, by restricting the pairwise relationships in the weight matrices. Specifically, neighborhood unions are introduced to employ the local class distribution and class size to constrain pairwise relationships in the weight matrices when classifying unbalanced sample sets. To evaluate the performances of LWDP in low dimensions, a holistic scoring scheme is proposed to stress the performances under low dimensions. In addition, overall accuracy curves and Kappa Index of Agreement (KIA) curves, which exhibit KIA in dimensions, are also used. The experimental results show that LWDP and its kernel extension outperform the other classic or latest methods in processing unbalanced sample set of VHR airborne imagery.
Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation ...
详细信息
Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation-it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://***. (C) 2011 Elsevier Ltd. All rights reserved.
This paper presents a scheme of a multiple-image compressed encryption based on the compressive holography technique. Computer generate hologram (CGH) is implemented to record multiple images simultaneously into an en...
详细信息
The image classifications techniques have been practiced by remotesensing experts following certain methods like unsupervised and supervised. Supervised classification requires precise human intervention to extract f...
详细信息
By considering the strong correlation between wavelet coefficients of the actual image, while bivariate model is only a statistical model for the interscale dependency of wavelet coefficient with parent coefficient, w...
详细信息
By considering the strong correlation between wavelet coefficients of the actual image, while bivariate model is only a statistical model for the interscale dependency of wavelet coefficient with parent coefficient, without taking into account the correlation of adjacent coefficient. Therefore, based on the shift-invariance and better directionality of the dual-tree complex wavelet transfer (DTCWT) and incorporating neighboring wavelet coefficients with BiShrink, a novel BiShrink threshold and DTCWT remotesensingimage denoising method is presented. Experimental results show the proposed algorithm gets better PSNR than other methods mentioned observably. In terms of visual quality the proposed algorithm can get the images with more details smooth profiles and aliasing is restricted
The rainfall process in autumn of Chengdu region has significant regional characteristics. Since GPS detection technology has characteristics such as all-weather, high accuracy, high spatial and temporal resolution an...
详细信息
ISBN:
(纸本)9780819485762
The rainfall process in autumn of Chengdu region has significant regional characteristics. Since GPS detection technology has characteristics such as all-weather, high accuracy, high spatial and temporal resolution and low cost, its tracking and monitoring technique on water vapor has achieved rapid developments in recent years. In this paper, it makes use of GPS-PWV data from 6 foundation GPS observation stations of GPS observation network in Chengdu region from September 2007 to November 2007 and from September 2008 to November 2008 these two falls which have 30min intervals. Fast Fourier transform was used to obtain the variation principle that in autumn the rainfalls change in the time period and there also has one quarter season within the fall, which is around 22 days or so. After the analysis, it finds that PWV drastically decreases at late October which is closely correlated to the local climate changing cycle. After we conduct composite analysis on diurnal cycle by integrating PWV with other meteorological elements we can find: There is a negative correlation between PWV and temperature;there is more obvious positive correlation between humidity and PWV. GPS precipitable water vapor may increase or maintain the characteristics within high value area from midnight to early morning which can bitterly correspond with the actual rainfall process of this region.
暂无评论