scholarly journals Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

2020 ◽  
Vol 12 (16) ◽  
pp. 2633
Author(s):  
Sergio R. Blanco ◽  
Dora B. Heras ◽  
Francisco Argüello
Keyword(s):  
Supervised Classification ◽  
Spatial Organization ◽  
Support Vector ◽  
Bag Of Words ◽  
Learning Machines ◽  
The Earth ◽  
Vector Machines ◽  
Texture Extraction ◽  
Vegetation Species

Texture information allows characterizing the regions of interest in a scene. It refers to the spatial organization of the fundamental microstructures in natural images. Texture extraction has been a challenging problem in the field of image processing for decades. In this paper, different techniques based on the classic Bag of Words (BoW) approach for solving the texture extraction problem in the case of hyperspectral images of the Earth surface are proposed. In all cases the texture extraction is performed inside regions of the scene called superpixels and the algorithms profit from the information available in all the bands of the image. The main contribution is the use of superpixel segmentation to obtain irregular patches from the images prior to texture extraction. Texture descriptors are extracted from each superpixel. Three schemes for texture extraction are proposed: codebook-based, descriptor-based, and spectral-enhanced descriptor-based. The first one is based on a codebook generator algorithm, while the other two include additional stages of keypoint detection and description. The evaluation is performed by analyzing the results of a supervised classification using Support Vector Machines (SVM), Random Forest (RF), and Extreme Learning Machines (ELM) after the texture extraction. The results show that the extraction of textures inside superpixels increases the accuracy of the obtained classification map. The proposed techniques are analyzed over different multi and hyperspectral datasets focusing on vegetation species identification. The best classification results for each image in terms of Overall Accuracy (OA) range from 81.07% to 93.77% for images taken at a river area in Galicia (Spain), and from 79.63% to 95.79% for a vast rural region in China with reasonable computation times.

scholarly journals Seeing through Deception: A Computational Approach to Deceit Detection in Spanish Written Communication

2013 ◽  
Vol 1 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Ángela Almela ◽  
Rafael Valencia-García ◽  
Pascual Cantos
Keyword(s):  
Support Vector Machines ◽  
Ad Hoc ◽  
Opinion Mining ◽  
Computational Approach ◽  
Support Vector ◽  
Bag Of Words ◽  
Written Communication ◽  
Forensic Linguistics ◽  
Vector Machines

The present paper addresses the question of the nature of deception language. Specifically, the main aim of this piece of research is the exploration of deceit in Spanish written communication. We have designed an automatic classifier based on Support Vector Machines (SVM) for the identification of deception in an ad hoc opinion corpus. In order to test the effectiveness of the LIWC2001 categories in Spanish, we have drawn a comparison with a Bag-of-Words (BoW) model. The results indicate that the classification of the texts is more successful by means of our initial set of variables than with the latter system. These findings are potentially applicable to areas such as forensic linguistics and opinion mining, where extensive research on languages other than English is needed.

scholarly journals Classification of hyperspectral endocrine tissue images using support vector machines

2020 ◽  
Vol 16 (5) ◽  
pp. 1-10
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Magarita Ivanova ◽  
Thomas Neumuth ◽  
Nada Rayes ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Endocrine Tissue ◽  
Vector Machines

scholarly journals Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4523 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordóñez ◽  
Fernando Sáchez-Lasheras ◽  
Javier Roca-Pardiñas ◽  
and Javier de Cos-Juez
Keyword(s):  
Random Forest ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Computing Time ◽  
Principal Component ◽  
Point Clouds ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

scholarly journals A note on supervised classification and Nash-equilibrium problems

2017 ◽  
Vol 51 (2) ◽  
pp. 329-341
Author(s):  
Nicolas Couellan
Keyword(s):  
Nash Equilibrium ◽  
Support Vector Machines ◽  
Supervised Classification ◽  
Dual Space ◽  
Equilibrium Problems ◽  
Support Vector ◽  
Generalized Nash Equilibrium ◽  
Class Separation ◽  
Vector Machines ◽  
Generalized Nash Equilibrium Problems

In this note, we investigate connections between supervised classification and (Generalized) Nash equilibrium problems (NEP & GNEP). For the specific case of support vector machines (SVM), we exploit the geometric properties of class separation in the dual space to formulate a non-cooperative game. NEP and Generalized NEP formulations are proposed for both binary and multi-class SVM problems.

scholarly journals Support vector machines for classification of input vectors with different metrics

2011 ◽  
Vol 61 (9) ◽  
pp. 2874-2878 ◽  
Author(s):  
L. Gonzalez-Abril ◽  
F. Velasco ◽  
J.A. Ortega ◽  
L. Franco
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

A Hybrid Methodology For On-Line Process Monitoring

2004 ◽  
Vol 2 (1) ◽  
Author(s):  
Rakesh Kumar ◽  
Avinash M. Jade ◽  
Valadi K. Jayaraman ◽  
Bhaskar D. Kulkarni
Keyword(s):  
Process Monitoring ◽  
Current Practice ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Hybrid Strategy ◽  
Vector Machines ◽  
On Line ◽  
Line Process

A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-à-vis current practice.

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