Abstract
The paper is focused on the analysis of classification possibilities of multisensor data with different spatial resolutions using combined classifiers based on Bayes approach with equal prior probabilities and on minimum of the Mahalanobis distance. The task set up for the 2014 IEEE GRSS Data Fusion Contest was chosen as an application example. High resolution RGB image and lower resolution thermal infrared image from the same urban area were processed to perform classification of each higher resolution pixel. Development of a fast and straightforward procedure was targeted and combined classifiers are proposed for that, exploiting spectral features from each data set separately. It is shown that data fusion can be achieved using the proposed classifiers and improvement of classification quality can be obtained with respect to the cases where only one of the data sets is used. The best classification results were obtained using the combined Bayes- type classifier that provided overall classification accuracy of about 95 % when the ground truth pixels from the high resolution RGB image were used both for design and testing. DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13333
Highlights
Remote sensing from airplanes and satellites has become a widely used tool for solving tasks in management of natural resources, urban planning, precision agriculture and other areas
LiDAR data can be processed to obtain a height model of the stand, while spectral data can be used to detect species or assess health of trees etc. Quite often in this case, data from two different sources should be used in a combined way to solve a specific task, i.e. data fusion should be performed during processing
Classification of multisensor data with different spatial resolutions is usually performed by combining outputs of separate classifiers each dealing with data from one sensor, or designing a single classifier operating with a fused image [3]
Summary
Remote sensing from airplanes and satellites has become a widely used tool for solving tasks in management of natural resources, urban planning, precision agriculture and other areas. Data from optical sensors are in general acquired in the form of threedimensional images where each pixel is related with its spatial coordinates calculated from simultaneously collected GPS information. Pixel size in this case depends on the Manuscript received January 5, 2015; accepted June 26, 2015. LiDAR and SAR data are usually preprocessed to obtain images characterizing geographical areas under study and are registered to geographical coordinates Pixel size in this case can be chosen in the preprocessing procedure but it is limited by the amount of collected data within the spatial unit. Otherwise pixels of these images cannot be properly related with physical objects observed and their combined use for analysis of these objects cannot be performed correctly
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