Evolving multi-spectral neural network classifier using a genetic algorithm

This paper presents a neural network approach to classify three-band RGB color images for automatic visual inspection of seed maize. A back-propagation neural network classifier was developed and tested. The effectiveness of the neural network classification was evaluated by comparing with two conventional statistical methods, minimum distance (MD) and maximum likelihood (ML) classifications. Experimental results showed that the BP neural network classifier, the MD classifier and ML classifier provided the overall accuracies of 93.4%, 91.5% and 96%, respectively. The neural network classifier showed a better performance than the MD and ML classifiers in classifying the shady boundaries or blurred edges of an interested class when the training samples were selected from the boundary areas of the class. This research indicated that neural networks are suitable for the pattern classification of RGB color images.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper is intended to compare the Naive bayes classifier for ball bearing fault diagnostic system with the back propagation neural network based on the f-folds feature extraction algorithm. The f-folds feature extraction algorithm has been used with different number of folders and clusters. The two classifiers have shown similar classification accuracies. The Naive bayes classifier has not shown any case of false negative or false positive classification. However, the back propagation neural network classifier has shown many cases of false positive and false negative classifications. Key words: Neural network classifier, Naive bayes classifier, diagnostic system, engineering materials.

The purpose of this research was to evaluate six classifiers applied to Landsat-7 data for accuracy of Level II land-cover categories in Ohio. These methods consist of (1) USGS National Land Cover Data; (2) the spectral angle mapper; (3) the maximum likelihood classifier; (4) the maximum likelihood classifier with texture analysis; (5) a recently introduced hybrid artificial neural network; (6) and a recently introduced modified image segmentation and object-oriented processing classifier. The segmentation object-oriented processing (SOOP) classifier outperformed all others with an overall accuracy of 93.8% and Kappa Coefficient of 0.93. SOOP was the only classifier to have by-class producer and user accuracies of 90% or higher for all land-cover categories. A modified artificial neural network (ANN) classifier had the second highest overall accuracy of 87.6% and Kappa of 0.85. The four remaining classifiers had overall accuracies less than 85%. The SOOP classifier was applied to Landsat-7 data to perform a level II land-cover classification for the state of Ohio.

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

It is a challenge to obtain accurate result in remote sensing images classification, which is affected by many factors. In this paper, aiming at correctly identifying land use types reflec ted in remote sensing images, support vector machine, maximum likelihood classifier, backpropagation neural network, fuzzy c-means, and minimum distance classifier were combined to construct three multiple classifier systems (MCSs). Two MCSs were implemented, namely, comparative major voting (CMV) and Bayesian average (BA). One method called WA-AHP was proposed, which introduced analytic hierarchy process into MCS. Classification results of base classifiers and MCSs were compared with the ground truth map. Accuracy indicators were computed and receiver operating characteristic curves were illustrated, so as to evaluate the performance of MCSs. Experimental results show that employing MCSs can increase classification accuracy significantly, compared with base classifiers. From the accuracy evaluation result and visual check, the best MCS is WA-AHP with overall accuracy of 94.2%, which overmatches BA and rivals CMV in this paper. The producer’s accuracy of each land use type proves the good performance of WA-AHP. Therefore, we can draw the conclusion that MCS is superior to base classifiers in remote sensing image classification, and WA-AHP is an efficient MCS.

The design, implementation, and preliminary testing of a computer system for automatic multispectral magnetic resonance imaging analysis is presented. The modular structure of the system permits easy comparison between various classification algorithms. The classification accuracy of traditional statistical pattern-recognition algorithms is compared to the results that can be obtained with neural networks of different topologies. Quantitative (confusion matrices) as well as visual (segmented images) results of a study performed on sets of normal and pathological images are presented. Images segmented with a neural network classifier (NNC) appear less noisy than images segmented with a maximum likelihood classifier (MLC), and it has been observed that the NNC is less sensitive to the selection of the training sets than the MLC

A detailed comparison of the backpropagation neural network and maximum-likelihood classifiers for urban land use classification is presented. Landsat Thematic Mapper images of Tucson, Arizona, and Oakland, California, were used for this comparison. For the Tucson image, the percentage of matching pixels in the two classification maps was only 64.5%, while for the Oakland image it was 83.3%. Although the test site accuracies of the two Tucson maps were similar, the map produced by the neural network was visually more accurate; this difference is explained by examining class regions and density plots in the decision space and the continuous likelihood values produced by both classifiers. For the Oakland scene, the two maps were visually and numerically similar, although the neural network was superior in suppression of mixed pixel classification errors. From this analysis, the authors conclude that the neural network is more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover spectral signatures. The differences between the two algorithms may be viewed, in part, as the differences between nonparametric (neural network) and parametric (maximum-likelihood) classifiers. Computationally, the backpropagation neural network is at a serious disadvantage to maximum-likelihood, taking nearly an order of magnitude more computing time when implemented on a serial workstation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Land use land cover (LULC) classification is an important research criterion for urban growth modelling. In this study, five different parametric classification algorithms (Maximum Likelihood Classifier, Mahalanobis Distance, Minimum Distance, Spectral Angle Mapper, Spectral Correlation Mapper) are used for the LULC classification of the Barrackpore subdivision of West Bengal state in India. Two different Landsat datasets (Landsat5 and Landsat8) are used for 2005 and 2020. Various algorithms have shown good accuracy for different LULC feature classes. Minimum Distance and Maximum Likelihood Classifier (MLC) has given the highest overall accuracy in 2005 and 2020, respectively. The best classification results are used for the change detection in the LULC classes over fifteen years (2005-2020). The classification result will help to choose a suitable algorithm for a regional level LULC study in further research. The change detection result indicates the need for a good growth pattern in the region.

A learning vector quantization(LVQ) neural network classifier is for the first time applied for SAR data classification, of which the training termination rule is modified to make it have both the ability of classification and class signature analysis. A very high land cover classification accuracy is achieved. Especially under the condition that texture is considered, almost all roads can be classified correctly, which cannot be identified by BP MLP neural network and ML classifier.

PurposeThe purpose of this paper is to apply an on‐line automatic inspection and measurement of surface defect of thin‐film transistor liquid‐crystal display (TFT‐LCD) panels in the polyimide coating process with a modified template matching method and back propagation neural network classification method.Design/methodology/approachBy using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.FindingsWith template match and neural network classification in the database of the system, the program judges the kinds of the target defects characteristics, finds out the central position of cell defect, and analyzes cell defects.Research limitations/implicationsThe recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method and strategy, using unsophisticated and economical equipment, is also verified. The proposed method provides highly accurate results with a low‐error rate.Practical implicationsIn terms of sample training, the principles of artificial neural network were used to train the sample detection rate. In sample analysis, character weight was implemented to filter the noise so as to enhance discrimination and reduce detection.Originality/valueThe paper describes how pre‐inspection image processing was utilized in collaboration with the system to excel the inspection efficiency of present machines as well as for reducing system misjudgment. In addition, the measure for improving cell defect inspection can be applied to production line with multi‐defects to inspect and improve six defects simultaneously, which improves the system stability greatly.

Remote Sensing (RS) and Geographic Information System (GIS) are important technologies for the sustainable management of ecological environment. In this paper, Landuse information is obtained by integrating the maximum likelihood classification (MLC) and neural network classification (NNC) method, which can realize the automatic extraction and recognition of land-use in Beijing City from remote sensing data. The characteristics of land-use and its ecological effects are quantified by developing regional ecological value index (EVI) and transfer rate of the ecological value (TREV). The results show that land-use information obtained by the combinative methodology of MLC and NNC is accurate, the land-use change closely associated with the rapid development and the urbanization. The EVI and TREV can make quantitative evaluation for the ecological environment and its evolution direction. From 1996 to 2005, the EVI in study area shows a downward trend. This result would be useful for establishing better future management strategies for ecological environment in urban-rural transition zones.

Feature subset selection is of immense importance in the field of data mining. The increased dimensionality of data makes testing and training of general classification method difficult. This paper presents the development of a model for classifying Pima Indian diabetic database (PIDD). The model consists of two stages. In the first stage, genetic algorithm (GA) and Correlation based feature selection have been used in a cascaded fashion. GA rendered global search of attributes with fitness evaluation effected by CFS. The second stage a fine tuned classification is done using artificial neural networks by making the feature subset elicited in the first stage as inputs for the network. Experimental results signify that the feature subset identified by the proposed filter when given as input to Back propagation neural network classifier, lead to enhanced classification accuracy.

Land cover classification is a complex process that may be affected by many factors. Since the first resource satellite was launched in 1972, the remote sensing community has witnessed the impressive progress in image classification methods, which is primarily driven by the advancement of remote sensing technology and computer technology. In recent years, non-parametric classifiers such as the neural network, the decision tree classifier and other classifiers have developed increasingly. Therefore, the four broadly used classification methods, which are Maximum Likelihood Classification (MLC), Self-Organized Neural Network (SONN), Support Vector Machine (SVM) and Decision Tree Classification (DTC) are firstly applied for the land cover remote sensing classification based on the multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) images of Heilongjiang area. The emphasis is placed on the comparison of the four classification methods and the techniques used for improving classification accuracy. Then, we compare the four classifiers through different aspects. Through the comparison, we got the conclusions: DTC is the best, and MLC as one of the classical methods is more stable than other three methods. Therefore, we make the land cover classification test over China using DTC and MLC methods and compare them again. We also believe that the conclusions we got in this paper are valuable for how to select an appropriate classifier in the similar applications.

There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

Considering the form diversity of tree species composition in the Bagong Mountain National Forest Park of China, we mapped tree species utilizing Machine Learning Algorithms (support vector machines (SVM) and random forest (RF) classifiers) based on the OHS-2 hyperspectral satellite image by different datasets which combined spectral information and hyperspectral-derived vegetation indices (VIs) for improving tree species classification and explored the best performance of them. To verify the improvement, the results of physically-based spectral classifiers (spectral angle mapper (SAM) and maximum likelihood (ML) classifiers) were applied to compare with the results of machine learning algorithms. The results indicated an overall accuracy of 94.01%, 96.08%, 82.9% and 79.3% for SVM, RF, SAM and ML classifiers of the best performance using different datasets. Highest accuracies resulted from two machine learning algorithms classifiers; SVM and RF compared to SAM and ML classifiers. Although SVM outperformed RF when using all hyperspectral bands and VIs, the overall accuracy of the RF classifier is higher when compared to the SVM classifier using VIs combined selected features. Meanwhile, the RF classifier performed better than SVM after removing the redundancy of spectral data in training samples. Moreover, the machine learning algorithms successfully classified a small number of tree species (Cedrus deodara and Pterocarya stenoptera C. DC.) in the study area, but the physical spectroscopy-based method failed to classify these species. Such integration strategy improved the effectiveness of enhancing the accuracy of tree species classification and mapping their distribution on broad spatial and temporal scales using machine learning algorithms and hyperspectral imagery.