Add to ORKGA method of classifying MSS data is proposed which has some advantages in that it does not contain the usual pre-processing constraints, and caters for variation and correlation in the data. In addition it is very economic in terms of computer time
The launch of the Landsat-4 satellite in July 1982 provided the first full coverage from space of th...
Classification of broad area features in satellite imagery is one of the most important applications...
Effective partitioning of feature space for high classification accuracy with due attention to rare ...
Presently, automatic classification of multispectral data images is most commonly effected on a poin...
Initially, methods for analyzing earth observational data involved the use of only spectral variatio...
Pattern recognition plays a central role in numerically oriented remote sensing systems. It provides...
Automatic processing of remotely sensed data has to date been constrained to using training sets to ...
Automatic processing of remotely sensed data has to date been constrained to using training sets to ...
This paper deals with the classification of objects into a limited number of classes. Objects are ch...
In a classification study, the problem of feature selection should be addressed in two separate case...
A new supervised classification method is developed for quantitative analysis of remotely-sensed mul...
The utilization of minimum distance classification methods in remote sensing problems, such as crop ...
Data obtained by Multispectral Scanner (MSS) or multiband photography for resources information ext...
Land use classification is an important part of many remote-sensing applications. A lot of research ...
The problem of classification is shared across various disciplines. Designing even less computation...
The launch of the Landsat-4 satellite in July 1982 provided the first full coverage from space of th...
Classification of broad area features in satellite imagery is one of the most important applications...
Effective partitioning of feature space for high classification accuracy with due attention to rare ...
Presently, automatic classification of multispectral data images is most commonly effected on a poin...
Initially, methods for analyzing earth observational data involved the use of only spectral variatio...
Pattern recognition plays a central role in numerically oriented remote sensing systems. It provides...
Automatic processing of remotely sensed data has to date been constrained to using training sets to ...
Automatic processing of remotely sensed data has to date been constrained to using training sets to ...
This paper deals with the classification of objects into a limited number of classes. Objects are ch...
In a classification study, the problem of feature selection should be addressed in two separate case...
A new supervised classification method is developed for quantitative analysis of remotely-sensed mul...
The utilization of minimum distance classification methods in remote sensing problems, such as crop ...
Data obtained by Multispectral Scanner (MSS) or multiband photography for resources information ext...
Land use classification is an important part of many remote-sensing applications. A lot of research ...
The problem of classification is shared across various disciplines. Designing even less computation...
The launch of the Landsat-4 satellite in July 1982 provided the first full coverage from space of th...
Classification of broad area features in satellite imagery is one of the most important applications...
Effective partitioning of feature space for high classification accuracy with due attention to rare ...