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搜索结果: 1-15 共查到-Principal Component Analysis相关记录46条 . 查询时间(0.093 秒)
To study the degradation of thermosetting polymers, we apply a novel method to simultaneously study the chemical structural changes and network topology: evolved gas analysis-mass spectrometry combine...
Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classi...
In Remote Sensing the various bands of multispectral data have not the same relevance in order to identify pixels inside a specific land cover class. The band algebra combines different images in orde...
Digital image processing has the ability to detect geological linear features on the image by using some algorithms. The most common algorithm which is used for this purpose is edge filtering. Gradien...
Principal Component Analysis (PCA) is often utilised in point cloud processing as provides an efficient method to approximate local point properties through the examination of the local neighbourhood...
In this paper, the Tasseled Cap Transformation (TCT) was applied to QuickBird multispectral images for extracting archaeological features linked to ancient human transformations of the landscape. The ...
India accounts for the world’s greatest concentration of coal fires which cause several devastating environmental effects. Only Jharia Coal Field (JCF) in Jharkhand (India) contains nearly half of su...
Nonlinear principal component analysis (NLPCA) was performed to examine the total electron content (TEC) anomalies for the China Wenchuan earthquake of May 12, 2008 (= 7.9). This was applied to global...
The presence of ionospheric perturbations in possible association with two huge earthquakes (Noto-hanto peninsula and Niigata-chuetu-oki earthquakes) in 2007 was studied on the basis of a conventional...
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However,PCA suffers from the fact that each principal component is a linear combination of all the or...
Today, colour or multichannel satellite and aerial images are increasingly becoming available due to the commercial availability of multispectral digital sensors and pansharpening function of the co...
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove...
The paper considers the problem of object segmentation and shape recognition in discrete noisy data. Two different algorithms combine region growing techniques with principal component analysis. The...
We report the results of spectroscopic mapping observations carried out toward small (1' × 1') regions within the supernova remnants W44, W28, IC 443, and 3C 391 using the Infrared Spectrograph (IRS) ...
In order to evaluate the groundwater pollution, the application of statistical principal components analysis (PCA) was used as one useful tool. PCA was based on the physical– chemical data of groun...

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