Generating Classification Rules from Training Samples

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 to validate the extracted rules. To validate extracted rules, we built a fuzzy inference system (FIS) using the extracted rules as a rule base and classified samples from the training set data. The results in terms of confusion matrices are presented in the paper

Water Quality Retrieval from Landsat TM Imagery

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 models are used to evaluate correlation between the water quality parameters and spectral reflectance values

Content-Based Image Retrieval Using Associative Memories

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 memory. We use generalized bi-directional associative memory (BAMg) to store and retrieve images from the database. We store and retrieve images based on association. We present three topologies of the generalized bi-directional associative memory that are similar to the local area network topologies: the bus, ring, and tree. We have developed software to implement the CBIR system. As an illustration, we have considered three sets of images. The results of our simulation are presented in the paper

Random Forest Algorithm for Land Cover Classification

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 classifiers. A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest with other commonly used algorithms such as the maximum likelihood, minimum distance, decision tree, neural network, and support vector machine classifiers

Fuzzy Neural Network Models For Multispectral Image Analysis

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 to extract fuzzy classification rules. During the training phase, the connection strengths are updated. After training, classification rules are extracted by backtracking along the weighted paths through the FNN. The extracted rules are then optimized using a fuzzy associative memory (FAM) bank. The data mining system described above is useful in many practical applications such as mapping, monitoring and managing our planet’s resources and health, climate change impacts and assessments, environmental change detection and military reconnaissance

Random Forest Algorithm for Land Cover Classification

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 classifiers. A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest with other commonly used algorithms such as the maximum likelihood, minimum distance, decision tree, neural network, and support vector machine classifiers

Multispectral Image Analysis using Decision Trees

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 and built decision trees using randomly-selected training samples with variable sample sizes. One of the ways to overcome the overfitting problem is pruning a decision tree. We have generated pruned trees with data sets of various sizes and compared the accuracy obtained with pruned trees to the accuracy obtained with full decision trees. Furthermore, we extracted knowledge regarding classification rules from the pruned tree. To validate the rules, we built a fuzzy inference system (FIS) and reclassified the dataset. In designing the FIS, we used threshold values obtained from extracted rules to define input membership functions and used the extracted rules as the rule-base. The classification results obtained from decision trees and the FIS are evaluated using the overall accuracy obtained from the confusion matrix

Multispectral Image Analysis using Convolution Neural Networks

Machine learning (ML) techniques are used often to classify pixels in multispectral images. Recently, there is growing interest in using Convolution Neural Networks (CNNs) for classifying multispectral images. CNNs are preferred because of high performance, advances in hardware such as graphical processing units (GPUs), and availability of several CNN architectures. In CNN, units in the first hidden layer view only a small image window and learn low level features. Deeper layers learn more expressive features by combining low level features. In this paper, we propose a novel approach to classify pixels in a multispectral image using deep convolution neural networks (DCNNs). In our approach, each feature vector is mapped to an image. We used the proposed framework to classify two Landsat scenes that are obtained from New Orleans and Juneau, Alaska areas. The suggested approach is compared with the commonly used classifiers such as the Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The proposed approach has shown the state-of-the-art results

Association-based image retrieval

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 interactive systems. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, we propose a new approach for image storage and retrieval called association-based image retrieval (ABIR). We try to mimic human memory. The human brain stores and retrieves images by association. We use a generalized bi-directional associative memory (GBAM) to store associations between feature vectors. The results of our simulation are presented in the paper

Association-based image retrieval for automatic target recognition.

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 ATR system is to access themost likely candidate model rapidly from a large database. In this paper we propose new architecture for a model-based ATR systemthat is based on association-based image retrieval. We try to mimic human memory. The human brain retrieves images by association. We use generalized bi-directional associative memories to retrieve associated images from the database. We use the ATR system to identify military vehicles from their two dimensional views