Add to ORKGA method of classification of digitized multispectral image data is described. It is designed to exploit a particular type of dependence between adjacent states of nature that is characteristic of the data. The advantages of this, as opposed to the conventional per point approach, are greater accuracy and efficiency, and the results are in a more desirable form for most purposes. Experimental results from both aircraft and satellite data are included
Adaptive data processing procedures are applied to the problem of classifying objects in a scene sca...
The possibility of approaching multispectral data without any assumption on its statistical nature i...
For classifying multispectral satellite images, a multilayer perceptron (MLP) is trained using eithe...
Presented here is an algorithm that partitions a digitized multispectral image into parts that corre...
Presented here is an algorithm that partitions a digitized multispectral image into parts that corre...
A method is presented for feature extraction of multispectral scanner data. Non-training data is use...
Presently, automatic classification of multispectral data images is most commonly effected on a poin...
The research described in the thesis is concerned with the analysis of imagery obtained from aircraf...
Method is combination of digital and optical techniques. Multispectral data is coded into binary mat...
Two parallel and overlapping approaches to classification of remotely sensed data with the aid of sp...
The new unsupervised classification technique for classifying multispectral remote sensing data whic...
The ambiguity in the object detection process can be reduced if the spatial dependencies, which exis...
Progress is reported in developing and testing methods of estimating, from multispectral scanner dat...
Once admitted the advantages of object-based classification compared to pixel-based classification;...
This paper deals with the classification of objects into a limited number of classes. Objects are ch...
Adaptive data processing procedures are applied to the problem of classifying objects in a scene sca...
The possibility of approaching multispectral data without any assumption on its statistical nature i...
For classifying multispectral satellite images, a multilayer perceptron (MLP) is trained using eithe...
Presented here is an algorithm that partitions a digitized multispectral image into parts that corre...
Presented here is an algorithm that partitions a digitized multispectral image into parts that corre...
A method is presented for feature extraction of multispectral scanner data. Non-training data is use...
Presently, automatic classification of multispectral data images is most commonly effected on a poin...
The research described in the thesis is concerned with the analysis of imagery obtained from aircraf...
Method is combination of digital and optical techniques. Multispectral data is coded into binary mat...
Two parallel and overlapping approaches to classification of remotely sensed data with the aid of sp...
The new unsupervised classification technique for classifying multispectral remote sensing data whic...
The ambiguity in the object detection process can be reduced if the spatial dependencies, which exis...
Progress is reported in developing and testing methods of estimating, from multispectral scanner dat...
Once admitted the advantages of object-based classification compared to pixel-based classification;...
This paper deals with the classification of objects into a limited number of classes. Objects are ch...
Adaptive data processing procedures are applied to the problem of classifying objects in a scene sca...
The possibility of approaching multispectral data without any assumption on its statistical nature i...
For classifying multispectral satellite images, a multilayer perceptron (MLP) is trained using eithe...