Classification of Pre-Filtered Multichannel Remote Sensing Images

Open acces: http://www.intechopen.com/books/remote-sensing-advanced-techniques-and-platforms/classification-of-pre-filtered-multichanel-rs-imagesInternational audienc

Classification of filtered multichannel images

International audienceA typical tendency in modern remote sensing (RS) is to apply multichannel systems. Images formed by them are in more or less degree noisy. Thus, their pre-filtering can be used for different purposes, in particular, to improve classification. In this paper, we consider methods of multichannel image denoising based on discrete cosine transform (DCT) and analyze how parameters of these methods affect classification. Both component-wise and 3D denoising is studied for three-channel Landsat test image. It is shown that for better determination of different classes, DCT based filters, both component-wise and 3D variants are efficient, but with a different tuning of involved parameters. The parameters can be optimized with respect to either standard MSE or metrics that characterize image visual quality. Best results are obtained with 3D denoising. Although the main conclusions basically coincide for both considered classifiers, Radial Basis Function Neural Network (RBF NN) and Support Vector Machine (SVM), the classification results appear slightly better with RBF NN for the experiment carried out in this paper

Performance evaluation for blind methods of noise characteristic estimation for TerraSAR-X images

International audienceEstimation of noise characteristics is used in various image processing tasks such as edge detection, filtering, reconstruction, compression and segmentation, etc. It is very desirable to have as accurate as possible estimated noise characteristics which influence the quality of further processing. This paper deals with evaluation of accuracy of earlier proposed methods for blind estimation of speckle characteristics. Evaluation is done for TerraSAR-X single-look amplitude images. It is shown that the obtained estimates depend upon image complexity. Besides, parameters of any estimation method influence accuracy (bias) as well. Finally, spatial correlation of noise is yet another factor affecting the obtained estimates. As it is demonstrated, blind estimation in aggregate allows to obtain the estimates of speckle variance with relative error up to 20%, which is appropriate for practical needs. Besides, if speckle variance is estimated, it becomes possible to get accurate estimates of noise spatial correlation in DCT domain. Such estimates can be used in e. g. DCT-based filtering of SAR images