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 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 of Remotely Sensed Crops

The range in topography, biodiversity, and agricultural technology has led to the emergence of precision agriculture. Precision agriculture is a farming management concept based on monitoring, measuring, and responding to crop variability. Computer vision, image analysis, and image processing are gaining considerable traction. For this paper, image analysis involves recognizing individual objects and providing insights from vegetation indices. The data acquired was remote-sensed multispectral images from blueberry, maguey, and pineapple. After computing vegetation indices, histograms were analyzed to choose thresholds. The masking of vegetation indices with threshold allowed the removal of areas with shadows and soil. The four leading vegetation indices used were the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Red Edge (NDRE), the Simple Ratio, the Red Edge Chlorophyll Index, and the Visible Atmospherically Resistant Index (SAVI). This research reviews literature for acquiring, preprocessing, and analyzing remote-sensed multispectral images in precision agriculture. It compiles the theoretical framework for analyzing multispectral data. Also, it describes and implements radiometric calibration and image alignment using the custom code from the MicaSense repository. As a result, it was possible to segment the blueberry, tequila agave, and pineapple plants from the background regardless of the noisy images. Non-plant pixels were excluded and shown as transparent by masking areas with shadows and low NDVI pixels, which sometimes removed plant pixels. The NDVI and NDRE helped identify crop pixels. On the other hand, it was possible to identify the pineapple fruits from the agave plantation using the SAVI vegetation index and the thresholding method. Finally, the work identifies the problems associated with an incorrect data acquisition methodology and provides suggestions.ITESO, A. C

Journal Staff

This book constitutes the refereed proceedings of the 18th Scandinavian Conference on Image Analysis, SCIA 2013, held in Espoo, Finland, in June 2013. The 67 revised full papers presented were carefully reviewed and selected from 132 submissions. The papers are organized in topical sections on feature extraction and segmentation, pattern recognition and machine learning, medical and biomedical image analysis, faces and gestures, object and scene recognition, matching, registration, and alignment, 3D vision, color and multispectral image analysis, motion analysis, systems and applications, human-centered computing, and video and multimedia analysis

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

LANDSAT and radar mapping of intrusive rocks in SE-Brazil

The feasibility of intrusive rock mapping was investigated and criteria for regional geological mapping established at the scale of 1:500,00 in polycyclic and polymetamorphic areas using the logic method of photointerpretation of LANDSAT imagery and radar from the RADAMBRASIL project. The spectral behavior of intrusive rocks, was evaluated using the interactive multispectral image analysis system (Image-100). The region of Campos (city) in northern Rio de Janeiro State was selected as the study area and digital imagery processing and pattern recognition techniques were applied. Various maps at the 2:250,000 scale were obtained to evaluate the results of automatic data processing

The Random Forest Algorithm with Application to Multispectral Image Analysis

The need for computers to make educated decisions is growing. Various methods have been developed for decision making using observation vectors. Among these are supervised and unsupervised classifiers. Recently, there has been increased attention to ensemble learning--methods that generate many classifiers and aggregate their results. Breiman (2001) proposed Random Forests for classification and clustering. The Random Forest algorithm is ensemble learning using the decision tree principle. Input vectors are used to grow decision trees and build a forest. A classification decision is reached by sending an unknown input vector down each tree in the forest and taking the majority vote among all trees. The main focus of this research is to evaluate the effectiveness of Random Forest in classifying pixels in multispectral image data acquired using satellites. In this paper the effectiveness and accuracy of Random Forest, neural networks, support vector machines, and nearest neighbor classifiers are assessed by classifying multispectral images and comparing each classifier\u27s results. As unsupervised classifiers are also widely used, this research compares the accuracy of an unsupervised Random Forest classifier with the Mahalanobis distance classifier, maximum likelihood classifier, and minimum distance classifier with respect to multispectral satellite data

Multispectral image analysis for the detection of diseases in coffee production

Coffee is produced in Latin America, Africa and Asia, and is one of the most traded agricultural products in international markets. The coffee agribusiness has been diversified all over the world and constitutes an important source of employment, income and foreign exchange in many producing countries. In recent years, its global supply has been affected by adverse weather factors and pests such as rust, which has been reflected in a highly volatile international market for this product [1]. This paper shows a method for the detection of coffee crops and the presence of pests and diseases in the production of these crops, using multispectral images from the Landsat 8 satellite

Study of the urban evolution of Brasilia with the use of LANDSAT data

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were focused in a whole and dynamic way by the utilization of MSS-LANDSAT images for June 1973, 1978 and 1983. In order to aid data interpretation, a registration algorithm implemented at the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained permitted an evaluation of the urban growth of Brasilia, taking as reference the proposed stated for the construction of the city