Peculiarities of Classification of Lossy Compressed Multichannel Remote Sensing Images Using Trained Neural Networks

A lot of modern remote sensing images are multichannel and, due to this as well as to high resolution, they occupy quite a large space. This causes problems in their transfer and storage and leads to the necessity to apply compression where lossy compression is mainly used. The compressed images can be then processed in different ways where classification is a typical operation for which trained neural networks are widely used. Classifier performance depends on many factors including what are the images employed in training. We have earlier shown that if compressed images are planned to be classified, it is worth using just compressed images for training. However, images used for training and employed in classification can be obtained by different compression techniques. Hence, in this paper, we analyze and compare the results of using the same and different coders for training and classified images. It is demonstrated that the difference in classification accuracy is not large if one uses the same coder compressed data for training and classification or if the compression techniques are different. The largest difference has been observed for the situations when one coder is DCT-based and the other coder is wavelet-based.