An SMP Soft Classification Algorithm for Remote Sensing

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over four minutes using 32 processors

Multispectral Image Analysis Using Random Forest

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 Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral images using various classifiers such as the maximum likelihood classifier, neural network, support vector machine (SVM), and Random Forest and compare their results

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

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

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

Urban land cover mapping using medium spatial resolution satellite imageries: effectiveness of Decision Tree Classifier

The study is inserted in the framework of information extraction from satellite imageries for supporting rapid mapping activities, where information need to be extracted quickly and the elimination, also if partially, of manual digitalization procedures, can be considered a great breakthrough. The main aim of this study was therefore to develop algorithms for the extraction of urban layer by means of medium spatial resolution Landsat data processing; Decision Tree classifier was investigated as classification techniques, thus it allows to extract rules that can be later applied to different scenes. In particular, the aim was to evaluate which steps to perform in order to obtain a good classification procedure, mainly focusing on processing that can be applied to images and on training set features. The training set was evaluated on the basis of the number of classes to use for its creation, together with the temporal extension of the training set and input attributes, while images were submitted to different kind of radiometric pre and post-processing. The aim was the evaluation of the best variables to set for the creation of the training set, to be used for the classifier generation. Above-mentioned variables were compared and results evaluated on the basis of reached accuracies. Data used for the validation were derived from the Digital Regional Technical Ma

Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

Estimation and Mapping the Rubber Trees Growth Distribution using Multi Sensor Imagery With Remote Sensing and GIS Analysis

The plantation of rubber tree in different countries throughout the world are expanded rapidly in areas that are not known before in planting such as these vegetation species. Estimating and mapping the distribution of rubber trees stand ages in these regions is very necessary to get better understanding of the effects of the changes of land cover on the Carbon and Water Cycle and also the productivity of the latex in different ages. Many remote sensing techniques that have been used to estimate the land cover / land use for mapping and monitoring the distribution of rubber trees growth based on different remote sensing classification algorithms (Maximum likelihood, SAM classification, Decision Tree and Mahalanobis Distance) with different types of data (Multispectral, Hyperspectral or statistical) by using many sensor

Classification and mapping of rangeland vegetation physiognomic composition using Landsat Enhanced Thematic Mapper and IKONOS imagery

Despite the significant role of vegetation maps in understanding and monitoring patterns of rangeland ecosystems, limited work has been done in mapping rangeland vegetation especially in Africa. In this study, characterisation of vegetation composition and assessment of Landsat ETM+ and IKONOS spectral discrimination effectiveness for mapping rangeland physiognomic vegetation cover types using both maximum (ML) likelihood and fuzzy classifiers was done in Rakai and Kiruhura districts, South Western Uganda. Plot vegetation species growth form, cover and height data were collected from 450 sampling sites based on eight spectral strata generated using unsupervised image classification. Field data were grouped at four levels of seven, six, three and two vegetation physiognomic classes which were subjected to both ML and fuzzy classification using both Landsat ETM+ and IKONOS. Results of mapping accuracy assessment showed that IKONOS imagery classification was more accurate than Landsat ETM+. Fuzzy classification was associated with significantly higher mapping accuracy than ML (p<0.01). The highest overall accuracy with ML was 62.8% and 76.2% for Landsat ETM+ and IKONOS compared to 66.4% and 81% respectively when using fuzzy classification. Vegetation composition in the study area was shifting from woody to herbaceous dominated cover with predominance of stress resistance grass species. Improvement in mapping accuracy when using fuzzy classifier in this study provides useful insights in the limitations of maximum likelihood. There is need to investigate other classifiers in order to improve rangeland vegetation mapping and monitoring