Remote Sensing: Digital Image Analysis
Remote Sensing: Digital Image Analysis
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Classification of honeybee pollen using a multiscale texture filtering scheme
Machine Vision and Applications
Analog Integrated Circuits and Signal Processing
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The use of black & white (B&W) air photographs for the production of historic land cover maps can be done by image classification, using additional texture features. In this paper we evaluate the importance of a number of parameters in the image classification process based on texture, such as the window size, angle and distance used to produce the texture features, the number of features used, the image quantization level and its spatial resolution. The evaluation was performed using five photographs from the 1950s. The influence of the classification method, the number of classes searched for in the images and the post-processing tasks were also investigated. The effect of each of these parameters for the classification accuracy was evaluated by cross-validation. The selection of the best parameters was performed based on the validation results, and also on the computation load involved for each case and the end user requirements. The final classification results were good (average accuracy of 85.7%, k = 0.809) and the method has proven to be useful for the production of historic land cover maps from B&W air photographs.