Classification of Compressed Multichannel Images and Its Improvement

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

“…So, it is possible to state that lossy compression providing PSNR-HVS-M about 40 dB does not lead to sufficient reduction of Pcc for the One can be interested in the behavior of P cc depending on compression. For original image P cc = 0.865; for compressed images it equals to 0.853, 0.854, 0.853, 0.849, 0.842, and 0.839 for PSNR-HVS-M equal to 45,42,39,36,33, and 30 dB, respectively. So, it is possible to state that lossy compression providing PSNR-HVS-M about 40 dB does not lead to sufficient reduction of P cc for the considered case.…”

“…One option is to train a classifier in advance using earlier acquired images, e.g., uncompressed ones stored for training or other purposes. Another option is to use a part of the obtained compressed image for classifier training and its use for entire image classification [39]. One can expect that classification results would be different where both options have both advantages and drawbacks.…”

“…Our experiments with real-life images have been done using a three-channel Landsat TM image earlier used in our studies [38,39]. This test image (shown in pseudo-color representation in Figure 7a) contains three component images acquired in optical bands with central wavelengths equal to 0.66 µm, 0.56 µm, and 0.49 µm, respectively.…”

“…The image fragments used in classifier training are shown in Figure 7b whilst the pixel used for verification of classifiers are marked by the same colors in Figure 7c. Details concerning the numbers of pixels are given in [39]. Class 1 is marked by red color, Class 2-by green, Class 3-by dark blue; Class 4-by yellow, Class 5-by azure.…”

“…A part of data concerning lossy compression of this image component-wise are presented in Table 13 (more details can be found in [39]). As one can see, to provide PSNR-HVS-M des = 42 dB, one has to use QS ≈ 17.3.…”