On the nature of models in remote sensing
Remote Sensing of Environment
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Processing of Remotely-Sensed Images: An Introduction
Computer Processing of Remotely-Sensed Images: An Introduction
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
The application of artificial neural networks to the analysis of remotely sensed data
International Journal of Remote Sensing
Classification of Landsat Thematic Mapper imagery for land cover using neural networks
International Journal of Remote Sensing
Harshness in image classification accuracy assessment
International Journal of Remote Sensing
International Journal of Remote Sensing
Environmental Modelling & Software
Edited nearest neighbor rule for improving neural networks classifications
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Computers and Electronics in Agriculture
| Hi-index | 0.00 |
Image classification is a complex process affected by some uncertainties and decisions made by the researchers. The accuracy achieved by a supervised classification is largely dependent upon the training data provided by the analyst. The use of representative training data sets is of significant importance for the performance of all classification methods. However, this issue is more important for neural network classifiers since they take each sample into consideration in the training stage. The representativeness is related to the size and quality of the training data that are highly important in assessing the accuracy of the thematic maps derived from remotely sensed data. Quality analysis of training data helps to identify outlier and mixed pixels that can undermine the reliability and accuracy of a classification resulting from an incorrect class boundary definition. Training data selection can be thought of as an iterative process conducted to form a representative data set after some refinements. Unfortunately, in many applications the quality of the training data is not questioned, and the data set is directly employed in the training stage. In order to increase the representativeness of the training data, a two-stage approach is presented, and performance tests are conducted for a selected region. Multi-layer perceptron model trained with backpropagation learning algorithm is employed to classify major land cover/land use classes present in the study area, the city of Trabzon in Turkey. Results show that the use of representative training data can help the classifier to produce more accurate and reliable results. An improvement of several percent in classification accuracy can make significant effect on the quality of the classified image. Results also confirm the value of visualization tools for the assessment of training pixels through decision boundary analysis.