REMOTE SENSING TECHNIQUES FOR OIL PALM AGE CLASSIFICATION USING LANDSAT-5 TM SATELLITE

This paper demonstrates the procedure to classify the age of oil palm trees using Landsat-5 TM (thematic mapper) remote sensing data. The study was conducted in two phases: phase I focuses on the the land cover classification, and phase II involves the oil palm age classification. Firstly,the region of interest (ROI) was identified and drawn in order to supply the training and testing pixels for the supervised classification. Maximum likelihood (ML) classifier was used for land cover classification. The land cover classification using the ML produces a good result with an overall accuracy of 85.51% and kappa coefficient of 0.8208. Meanwhile, three classifiers were used to investigate the age of oil palm classification, which are the 1) Maximum likelihood (ML), 2) Neural Network (NN) and, 3) Support Vector Machine (SVM). The accuracy of the classifications was then assessed by comparing the classifications with a reference set using a confusion matrix technique. Among the three classifiers, SVM performs the best with the highest overall accuracy of 54.18% and kappa coefficient of 0.39

Computationally Inexpensive Sequential Forward Floating Selection for Acquiring Significant Features for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. This paper proposes an improved Sequential Forward Floating Selection method besides the exploration of significant features for invarianceness of authorship from global shape features by using various wrapper feature selection methods. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship

Feature Selection Methods for Writer Identification: A Comparative Study

Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper feature selection methods are selected and their performances are analyzed using image dataset from IAM Handwriting Database. It is also analyzed the number of features selected and the accuracy of these methods, and then evaluated and compared each method on the basis of these measurements. The evaluation identifies the most interesting method to be further explored and adapted in the future works to fully compatible with Writer Identification domain

Computational Intelligence In Digital Forensics: Forensic Investigation And Applications

The Series "Studies in Computational Intelligence" publishes new development and advances in the various areas of computational intelligence - quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output

Selecting Significant Features for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. It relates to invarianceness of authorship where invarianceness between features for intraclass (same writer) is lower than inter-class (different writer). This paper discusses and reports the exploration of significant features for invarianceness of authorship from global shape features by using feature selection technique. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship

Feature selection methods for writer identification: a comparative study

Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper feature selection methods are selected and their performances are analyzed using image dataset from IAM Handwriting Database. It is also analyzed the number of features selected and the accuracy of these methods, and then evaluated and compared each method on the basis of these measurements. The evaluation identifies the most interesting method to be further explored and adapted in the future works to fully compatible with Writer Identification domain

A Comparative Study of Feature Extraction Using PCA and LDA for Face Recognition

Feature extraction is important in face recognition. This paper presents a comparative study of feature extraction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. The evaluation parameters for the study are time and accuracy of each method. The experiments were conducted using six datasets of face images with different disturbance. The results showed that LDA is much better than PCA in overall image with various disturbances. While in time taken evaluation, PCA is faster than LDA

Optimizing Feature Extraction using PSO-LDA for Face Recognition

Feature extraction is one of important process in face recognition LDA is dimensional reduction techniques that commonly used as feature extraction. Feature extraction by using LDA will produce feature space to extract important information of data. Selecting number of eigenvector which are used as feature space will not only effect on the computational time, but also effect on the recognition rate. This paper presents analysis number of eigenvector which is potential used as parameter extraction in feature extraction. The main idea of applying PSO in LDA is to search the number of parameter extraction for the optimal feature subset where features are carefully selected according to a well-defined discrimination criterion. Hybridizing PSO and LDA conducted to find the best number of feature space in order to optimize the recognition rate. The experiment conducted by using four databases with different disturbance, i.e. luminance, expression, focus and background, and also random disturbance. The results show that PSO-LDA can obtain the minimum number of parameter extraction which produces the highest recognition rate

PSO and Computationally Inexpensive Sequential Forward Floating Selection in Acquiring Significant Features for Handwritten Authorship

The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification in order to find the most significant features. This paper proposes a hybrid feature selection method of Particle Swarm Optimization and Computationally Inexpensive Sequential Forward Floating Selection for Writer Identification. The promising applicability of the proposed method has been demonstrated and worth to receive further exploration in identifying the handwritten authorship

A Comparative Study of Feature Selection Methods for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification. Various filter and wrapper feature selection methods are selected and their performances are analyzed. This paper describes an improved sequential forward feature selection method besides the exploration of significant features for invarianceness of authorship from global shape features by using various feature selection methods. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship