Methods for noise reduction in multicomponent spectral images are developed and discussed. Multicomponent spectral images can be corrupted by noise either on all the channels or on some of the channels only. In the first case there are two possibilities: either the noise is on all the channels in the same way or the noise is randomly distributed on all the channels. We studied two methods for noise reduction directly on the multicomponent spectral image: the vector median filter and our new method, the spectrum smoothing, which does not care about neighbouring pixels but tries to reduce noise on one pixel at a time. The idea behind spectrum smoothing lies on the nature of a color spectrum. Color spectrum is naturally smooth, and does not have any peaks, unlike a noisy spectrum would have. If some of the channels are noisy, there is a problem of
finding the noisy channels. We came into a conclusion that if a channel correlates poorly with the neighboring channel,
the channel can be considered noisy, and filtering is applied to that channel. Results from our new spectrum smoothing filter were very promising for Gaussian noise compared to Gaussian 3 by 3 filter and mean 5 by 5 filter.
Several powerful lossy compression methods have been developed for hyperspectral images. However, it is difficult to determine sufficient quality for reconstructed hyperspectral images. We have measured the information loss from the lossy compression with Signal-to-Noise-Ratio (SNR) and Peak-Signal-to-Noise-Ratio (PSNR). To get more illustrative error measures unsupervised K-means clustering combined with spectral matching methods was used. Spectral matching methods include Euclidean distance, Spectral Similarity Value (SSV) and Spectral Angle Mapper (SAM). We used two AVIRIS radiance images, which were compressed with three different methods: the Self-Organizing Map (SOM), Principal Component Analysis (PCA) and three-dimensional wavelet transform combined with lossless BWT/Huffman encoding. The two-dimensional JPEG2000 compression method was applied to the eigenimages produced by the PCA. It was found that clustering combined with spectral matching is a good method to realize the image quality for many applications. The high classification accuracies have been achieved even at very high compression ratios. The SAM and the SSV are much more vulnerable for information loss caused by the lossy compression than the Euclidean distance. The results suggest that lossy compression is possible in many real-world segmentation applications. The PCA transform combined with JPEG2000 was the best compression method according to all error metrics.
This paper proposes an interband version of the linear prediction approach for hyperspectral images. Linear prediction represents one of the best performing and most practical and general purpose lossless image compression techniques known today. The interband linear prediction method consists of two stages: predictive decorrelation producing residuals and entropy coding of the residuals. Our method achieved a compression ratio in the range of 3.02 to 3.14 using 13 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images.
We have composed several lossy compression methods for multispectral images. These methods include the Self-Organizing Map (SOM), Principal Component Analysis (PCA) and the three-dimensional wavelet transform combined with traditional lossless coding methods. The two-dimensional DCT/JPEG, JPEG2000 and SPIHT compression methods were applied to eigenimages produced by the PCA. The information loss from the compression was measured with Signal-to-Noise-Ratio (SNR) and Peak-Signal-to-Noise ratio (PSNR). To get more illustrative error measures C-means clustering and Euclidean distance for spectral matching were used. The test image was an AVIRIS image with 224 bands and 512 lines in 614 columns. The PCA in the spectral dimension was the best method in terms of image quality and compression speed. If required, JPEG2000 or SPIHT can be applied in spatial dimensions to get better compression ratios.
In this paper, a new group of noise reduction methods for multispectral images is presented. First, a 1-dimensional Self-Organizing Map (SOM) is taught using the pixel vectors of the noisy multispectral image. Then, a gray-level index image is formed containing the indexes of the SOM vectors. Several gray-level noise reduction methods are applied to the index image for three noise types: impulse, Gaussian, and coherent noise. Tests are made for three kinds of noise distrubutions: for all channels, for channels 30-50, and for 9 selected channels. Error measures imply that the obtained results are very good for coherent noise images, but rather poor for other noise categories, compared to the bandwise coherent filter.
The problem of selecting an appropriate wavelet filter is always present in signal compression based on the wavelet transform. In this report, we give a method to select a wavelet filter for multispectral image compression. The wavelet filter selection is based on the Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectral images. When a new image is to be compressed, a set of spectra from that image is selected, the spectra are classified by the trained LVQ and the filter associated to the largest class is selected for the compression of the whole multispectral image. The results show, that our method finds the most suitable wavelet filter for compression of multispectral images.