The application of artificial neural networks to the analysis of remotely sensed data
Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely
sensed data. Although significant progress has been made in image classification based …
sensed data. Although significant progress has been made in image classification based …
[BOOK][B] Computer processing of remotely-sensed images
Computer Processing of Remotely-Sensed Images A thorough introduction to computer
processing of remotely-sensed images, processing methods, and applications Remote …
processing of remotely-sensed images, processing methods, and applications Remote …
[BOOK][B] Classification methods for remotely sensed data
P Mather, B Tso - 2016 - taylorfrancis.com
Since the publishing of the first edition of Classification Methods for Remotely Sensed Data
in 2001, the field of pattern recognition has expanded in many new directions that make use …
in 2001, the field of pattern recognition has expanded in many new directions that make use …
Feature selection for classification of hyperspectral data by SVM
Support vector machines (SVM) are attractive for the classification of remotely sensed data
with some claims that the method is insensitive to the dimensionality of the data and …
with some claims that the method is insensitive to the dimensionality of the data and …
Map** roofing with asbestos-containing material by using remote sensing imagery and machine learning-based image classification: A state-of-the-art review
Building roofing produced with asbestos-containing materials is a significant concern due to
its detrimental health hazard implications. Efficiently locating asbestos roofing is essential to …
its detrimental health hazard implications. Efficiently locating asbestos roofing is essential to …
The use of backpropagating artificial neural networks in land cover classification
Artificial neural networks (ANNs) are used for land cover classification using remotely
sensed data. Training of a neural network requires that the user specifies the network …
sensed data. Training of a neural network requires that the user specifies the network …
Computer assisted customer churn management: State-of-the-art and future trends
A business incurs much higher charges when attempting to win new customers than to
retain existing ones. As a result, much research has been invested into new ways of …
retain existing ones. As a result, much research has been invested into new ways of …
Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of …
The main objective of the present study was to utilize Genetic Algorithms (GA) in order to
obtain the optimal combination of forest fire related variables and apply data mining …
obtain the optimal combination of forest fire related variables and apply data mining …
Selecting optimal conditioning factors in shallow translational landslide susceptibility map** using genetic algorithm
Many landslide conditioning factors have been considered in the literature for landslide
susceptibility map**, but it is not certain which factors produce the best result for an area …
susceptibility map**, but it is not certain which factors produce the best result for an area …
Deep learning-based extraction of construction procedural constraints from construction regulations
Construction procedural constraints are critical in facilitating effective construction procedure
checking in practice and for various inspection systems. Nowadays, the manual extraction of …
checking in practice and for various inspection systems. Nowadays, the manual extraction of …