Physical Sciences and Mathematics Commons^™

Multispectral Image Analysis Using Decision Trees, Arun D. Kulkarni, Anmol Shrestha

Arun Kulkarni

Many machine learning algorithms have been used to classify pixels in Landsat imagery. The maximum likelihood classifier is the widely-accepted classifier. Non-parametric methods of classification include neural networks and decision trees. In this research work, we implemented decision trees using the C4.5 algorithm to classify pixels of a scene from Juneau, Alaska area obtained with Landsat 8, Operation Land Imager (OLI). One of the concerns with decision trees is that they are often over fitted with training set data, which yields less accuracy in classifying unknown data. To study the effect of overfitting, we have considered noisy training set data …

Generating Classification Rules From Training Samples, Arun D. Kulkarni

Arun Kulkarni

In this paper, we describe an algorithm to extract classification rules from training samples using fuzzy membership functions. The algorithm includes steps for generating classification rules, eliminating duplicate and conflicting rules, and ranking extracted rules. We have developed software to implement the algorithm using MATLAB scripts. As an illustration, we have used the algorithm to classify pixels in two multispectral images representing areas in New Orleans and Alaska. For each scene, we randomly selected 10 per cent of the samples from our training set data for generating an optimized rule set and used the remaining 90 per cent of samples …

Knowledge Extraction From Metacognitive Reading Strategies Data Using Induction Trees, Christopher Taylor, Arun D. Kulkarni, Kouider Mokhtari

Arun Kulkarni

The assessment of students’ metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, …

Water Quality Retrieval From Landsat Tm Imagery, Arun D. Kulkarni

Arun Kulkarni

In this paper, the utility of Landsat TM imagery for water quality studies in East Texas is investigated. Remote sensing has an important and effective role in water quality management. Remote sensing satellites measure the amount of solar radiation reflected by surface water and the reflectance of water depend upon the concentration and character of water quality parameters. Three water quality parameters namely the total suspended solids, chlorophyll-a, and turbidity are estimated in this study. In situ water quality parameter measurements from seven ground stations and the corresponding Landsat TM data were used to estimate the water quality parameters. Regression …

Association-Based Image Retrieval For Automatic Target Recognition., Arun D. Kulkarni, H. Gunturu, S. Dalta

Arun Kulkarni

Model-based automatic target recognition (ATR)systems deal with recognizing three dimensional objects from two dimensional images. In order to recognizeand identify objects the ATRsystem must have one or more stored models. Multiple two dimensional views of each three dimensional objectthat may appear in the universe it deals withare stored in the database. During recognition, two dimensional view of atarget object is used a query image and the search is carried out to identify the corresponding three dimensional object. Stages of a model-based ATR system include preprocessing, segmentation, feature extraction, and searching thedatabase. One of the most important problems in a model-based …

Fuzzy Neural Network Models For Multispectral Image Analysis, Arun D. Kulkarni, Sara Mccaslin

Arun Kulkarni

Fuzzy neural networks (FNNs) provide a new approach for classification of multispectral data and to extract and optimize classification rules. Neural networks deal with issues on a numeric level, whereas fuzzy logic deals with them on a semantic or linguistic level. FNNs synthesize fuzzy logic and neural networks. Recently, there has been growing interest in the research community not only to understand how FNNs arrive at particular decisions but how to decode information stored in the form of connection strengths in the network. In this paper, we propose fuzzy neural network models for classification of pixels in multispectral images and …

Random Forest Algorithm For Land Cover Classification, Arun D. Kulkarni, Barrett Lowe

Arun Kulkarni

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree …

Assessing Metacognitive Skills Using Adaptive Neural Networks, Anderson Justin, Kouider Mokhtari, Arun Kulkarni

Arun Kulkarni

The assessment of student's levels of metacognitive knowledge and skills is critical in determining their ability to effectively perform complex cognitive tasks such as solving mathematics or reading comprehension problems. In this paper, we use an adaptive multiplayer perceptron model to categorize participants based on their metacognitive awareness and perceived use of reading strategies while reading. Eight hundred and sixty-five middle school students participated in the study. All participants completed a 30-item instrument- the Metacognitive Awareness-of-Reading Strategies Inventory (MARSI). We used adaptive multi-layer perceptron models to classify participants into three groups based on their metacognitive strategy awareness levels using thirteen …

Knowledge Extraction From Survey Data Using Neural Networks, Khan Imran, Arun Kulkarni

Arun Kulkarni

Surveys are an important tool for researchers. It is increasingly important to develop powerful means for analyzing such data and to extract knowledge that could help in decision-making. Survey attributes are typically discrete data measured on a Likert scale. The process of classification becomes complex if the number of survey attributes is large. Another major issue in Likert-Scale data is the uniqueness of tuples. A large number of unique tuples may result in a large number of patterns. The main focus of this paper is to propose an efficient knowledge extraction method that can extract knowledge in terms of rules. …

Association-Based Image Retrieval, Arun D. Kulkarni, H. Gunturu, S. Dalta

Arun Kulkarni

With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content-based image retrieval (CBIR) systems have been developed in the last decade. A typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the database with similar features. Recent advances in CBIR systems include relevance feedback based …

Multispectral Image Analysis Using Random Forest, Barrett Lowe, Arun Kulkarni

Arun Kulkarni

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin 2001 for classification and clustering. Random Forest grows many decision trees for classification. To classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a classification. The forest chooses the classification having the most votes. Random …

Content-Based Image Retrieval Using Associative Memories, Arun D. Kulkarni

Arun Kulkarni

The rapid growth in the number of large-scale repositories has brought the need for efficient and effective content-based image retrieval (CBIR) systems. The state of the art in the CBIR systems is to search images in database that are “close” to the query image using some similarity measure. The current CBIR systems capture image features that represent properties such as color, texture, and/or shape of the objects in the query image and try to retrieve images from the database with similar features. In this paper, we propose a new architecture for a CBIR system. We try to mimic the human …

Association-Based Image Retrieval, Arun D. Kulkarni

Arun Kulkarni

No abstract provided.

Association-Based Image Retrieval, Arun D. Kulkarni, H. Gunturu, S. Datla

Arun Kulkarni

No abstract provided.

Association-Based Image Retrieval, Arun D. Kulkarni, Harikrisha Gunturu, Srikanth Datla

Arun Kulkarni

Fuzzy Neural Network Models For Classification, Arun D. Kulkarni, Charles D. Cavanaugh

Arun Kulkarni

Fuzzy Neural Network Models For Supervised Classification: Multispectral Image Analysis, Arun D. Kulkarni, Kamlesh Lulla

Arun Kulkarni

No abstract provided.

Solving Ill-Posed Problems With Artificial Neural Networks, Arun D. Kulkarni

Arun Kulkarni

Quantitative Evaluation Of Enhancement Techniques, B. L. Deekshatulu, Arun D. Kulkarni, G. Kashipati Rao

Arun Kulkarni