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 …
A critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations
Abstract The continuing Machine Learning (ML) revolution indubitably has had a significant
positive impact on the analysis of downlinked satellite data. Other aspects of the Earth …
positive impact on the analysis of downlinked satellite data. Other aspects of the Earth …
How many hidden layers and nodes?
D Stathakis - International Journal of Remote Sensing, 2009 - Taylor & Francis
The question of how many hidden layers and how many hidden nodes should there be
always comes up in any classification task of remotely sensed data using neural networks …
always comes up in any classification task of remotely sensed data using neural networks …
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 …
A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification
The successful launch of panchromatic WorldView-1 and the planned launch of WorldView-
2 will make a major contribution towards the advancement of the commercial remote …
2 will make a major contribution towards the advancement of the commercial remote …
Fusion of support vector machines for classification of multisensor data
The classification of multisensor data sets, consisting of multitemporal synthetic aperture
radar data and optical imagery, is addressed. The concept is based on the decision fusion of …
radar data and optical imagery, is addressed. The concept is based on the decision fusion of …
Extracting urban features from LiDAR digital surface models
G Priestnall, J Jaafar, A Duncan - Computers, Environment and Urban …, 2000 - Elsevier
The use of airborne Light Detection And Ranging (LiDAR) technology offers rapid high
resolution capture of surface elevation data suitable for a large range of applications. The …
resolution capture of surface elevation data suitable for a large range of applications. The …
Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics
Empirical models designed to simulate and predict urban land‐use change in real situations
are generally based on the utilization of statistical techniques to compute the land‐use …
are generally based on the utilization of statistical techniques to compute the land‐use …