spectral feature
Dark endmember in spectral feature space under variable atmosphere
... n-D spectral feature space, and 2) it passes through a DPVE as it becomes darker until no direct ...of spectral feature space enables conventional multispectral image transformation methods to ...
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Mapping of Mineral Zones using the Spectral Feature Fitting Method in Jahazpur belt, Rajasthan, India
... a spectral resolution so highly useful for the mapping of the minerals, lithological units, and different regional ...structures. Spectral Feature Fitting (SFF) algorithm is used for the processing ...
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An Improved Spectral Feature Alignment for Domain Adaptation in Sentiment Classification
... improved spectral feature alignment domain adaptation algorithm (ISFA) based on the SFA ...new feature representation to obtain a new feature representation ...
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Environmental Sound Perception: Metadescription and Modeling Based on Independent Primary Studies
... a spectral feature that most often represented auditory brightness (energy distribution along the frequency scale), a temporal feature that characterized attack, and a spectro-temporal feature ...
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Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images
... raw spectral data, fusion of the spectral-spatial features can be applied into the HSI ...spatial feature and spectral feature together, the derived high dimensional data may lead the ...
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Two-colour quantum entanglement in a singly resonant optical parametric oscillator approaching threshold
... and spectral broadening of the intensity dif- ference for different values of g is presented in ...One spectral feature that is instead peculiar to the singly resonant case is that of the ...
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Mapping of Aluminous Rich Laterite Depositions through Hyper Spectral Remote Sensing
... dataset. Spectral subsetting of noise prone bands has been successfully ...filter. Spectral signatures of sam- ples were derived from the processed ...data. Spectral signature of each sample and ...
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Multi Spectral Inter-Correlative Approach for Feature Selection in Pattern Recognition
... to feature selection based on inter corrective property of band coefficient in spectral feature ...finer feature selection, minimizing the redundant band ...multi spectral band ...
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Design and Software Implementation Of Efficient Speech Recognizer
... 100 feature vectors per second) is generally adequate for ...most spectral feature extract techniques has the dimension of 30 to ...of feature dimension is not necessary ...
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Educational Data Clustering in a Weighted Feature Space Using Kernel K-Means and Transfer Learning Algorithms
... and spectral feature alignment algorithms in our paper as a solution to the educational data clustering task in such a ...weighted feature space so that how much contribution of the larger source ...
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Improving The SFA Algorithm by Employing Multi-Source Data
... on Spectral Feature Alignment algorithm, trained with several sources is used to classify opinions of another different ...same. Spectral clustering is used to adapt domain-independent and ...
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Real-time target detection in hyperspectral images based on spatial-spectral information extraction
... Isometric Feature Mapping [16], Diffusion map, Locally linear embedding [17], Local Tangent Space Alignment [18], and so ...lower spectral resolution bands ...the spectral features of the specific ...
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A DCT-based Local Feature Extraction Algorithm For Palm-print Recognition
... a feature vector with a very large ...the feature dimension, we propose to utilize the magnitudes corresponding to the dominant DCT coefficients as spectral ...desired feature, which also ...
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Multi-Channel Image Denoising In Local Spectral Component Decomposition G. Shankara Bhaskara Rao
... generated spectral line vectors are not smooth (top ...the spectral line components and the filtered result are affected and fail to preserve the ...the spectral line ...
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Signal Type Detection in CRN :A Machine Learning Framework Using Spectral Correlation Feature
... In this paper we try to investigate the use of spectral cyclic analy- sis for classifying a group of modulation schemes in a hierachical fashion.The proposed framework will attempt to distinguish some signal of ...
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Hyperspectral image spectral spatial feature extraction via tensor principal component analysis
... The extractors include two classical vector-based algorithms — PCA and LDA, and three state-of-the-art tensor-based algorithms — TDLA [6], LTDA [7] and TPCA (ours). We also give the quantitative results obtained by the ...
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Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images
... rich spectral and spatial information thus providing a very useful source for scene ...closeness, spectral Euclidean distance and spectral angle ...both spectral intensity differences together ...
137
Face Biometrics Using Enhanced Version of Genetic Algorithm
... Taabish Gulzar et al.(2014), has discussed on Linear Prediction Cepstral Coefficient (LPCC),Mel Frequency Cepstral Coefficient (MFCC),Bark Frequency Cepstral Coefficient (BFCC) feature extraction techniques for ...
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A ROBUST FRAMEWORK TO DETECT MOVING VEHICLES IN DIFFERENT ROAD CONDITIONS IN INDIA
... various spectral coefficients can be further correlated with the values, characterizing the physical and chemical properties of the analyzed material: reactivity, porosity, diffusion coefficient, and the so ...
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Image quality assessment for fused remote sensing imageries
... Figure 7 shows classified image of different fusion technique respectively. To validate the fusion results, confusion matrix was computed for each classification image and overall accuracy is determined in Table 3. Based ...
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