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

Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments

By extending the previously proposed geometric branch-and-bound algorithm with bounded alignment for point pattern matching, the paper presents the development and evaluation of a new and fast algorithm for image registration based on line segments. Using synthetically generated data sets with randomly distributed line segments and hard test cases with highly symmetric line patterns, as well as real remote sensing images, the developed algorithm is shown to be computationally fast, highly robust, capable of handling severely corrupted data sets with considerable line segment position errors as well as significant fragmented and spurious line segments in the images to be matched

A new DCT-based multiresolution method for simultaneous denoising and fusion of SAR images

Individual multiresolution techniques for separate image fusion and denoising have been widely researched. We propose a novel multiresolution discrete cosine transform based method for simultaneous image denoising and fusion, demonstrating its efficacy with respect to discrete wavelet transform and dual-tree complex wavelet transform. We incorporate the Laplacian pyramid transform multiresolution analysis and a sliding window discrete cosine transform for simultaneous denoising and fusion of the multiresolution coefficients. The impact of image denoising on the results of fusion is demonstrated and advantages of simultaneous denoising and fusion for SAR images are also presented

Assessment of soil parameter estimation errors for fusion of multichannel radar measurements

The application of multichannel radar measurement techniques for estimation of bare soil parameters is based on different principles of radiowave and soil surface interaction depending on radiowave frequency, polarisation and incidence angle. The accuracy of soil parameter estimation depends on the number of radar measurements and the choice of radiowave parameters. Random and systematic errors present in radar data may also have the impact on estimation results. To improve the accuracy of soil parameters estimation by fusion of multichannel radar data we propose a new method for assessment of estimation errors. It is based on local linear approximation of the radiowave scattering model and takes into account impairment characteristics, measurement conditions and radar parameters. This new method is applied to an example to illustrate how the estimation accuracy of soil moisture and roughness parameters can be improved by optimising the radar operating frequencie

Robust processing of SAR hologram data to mitigate impulse noise impairments

The standard linear algorithm for synthetic aperture radar (SAR) image synthesis consists of fusing the real and imaginary components of the hologram data, and applying a matched filter to the radar returns from each point to generate the SAR image. This algorithm becomes inefficient when the SAR hologram data are corrupted by impulse noise due to data transmission and coding/decoding errors. The degradation of synthesized SAR images in the presence of impulse noise in the hologram data is considered and new nonlinear algorithms based on robust estimation are proposed. Experimental results for measured SAR data are presented. It is shown that the proposed algorithms efficiently reject impulse noise and substantially improve SAR image qualit

Mitigation of sensor and communication system impairments for multichannel image fusion and classification

The impairments produced in image sensors and communication channels degrade image quality and introduce significant errors in the results of data fusion. To improve image fusion results in the presence of different types of impairments we propose a two-stage approach. New multistage nonlinear locally-adaptive image processing algorithms are developed and applied at the first stage to mitigate image impairments such as geometric distortions due to communication system synchronization errors, narrowband frequency interferences and sensor noise. Image fusion and classification algorithms based on artificial neural networks and support vector machines are used at the second stage. Experimental results are presented for real satellite remote sensing images and simulated data providing quantitative assessment of the proposed algorithm

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