Processing of Multichannel Remote-Sensing Images with Prediction of Performance Parameters

In processing of multichannel remote sensing data, there is a need in automation of basic operations as filtering and compression. Automation presumes undertaking a decision on expedience of image filtering. Automation also deals with obtaining of information based on which certain decisions can be undertaken or parameters of processing algorithms can be chosen. For the considered operations of denoising and lossy compression, it is shown that their basic performance characteristics can be quite easily predicted based on easily calculated local statistics in discrete cosine transform (DCT) domain. The described methodology of prediction is shown to be general and applicable to different types of noise under condition that its basic characteristics are known in advance or pre-estimated accurately

Automatic Adaptive Lossy Compression of Multichannel Remote Sensing Images

In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted

Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification

This chapter addresses an important practical task of classification of multichannel remote sensing data with application to multitemporal dual-polarization Sentinel radar images acquired for agricultural regions in Ukraine. We first consider characteristics of dual-polarization Sentinel radar images and discuss what kind of filters can be applied to such data. Several examples of denoising are presented with analysis of what properties of filters are desired and what can be provided in practice. It is also demonstrated that the use of preliminary denoising produces improvement of classification accuracy where despeckling that is more efficient in terms of standard filtering criteria results in better classification

Image Quality Prediction for DCT-based Compression

International audiencea method for prediction and providing compressed image quality for lossy compression techniques based on discrete cosine transform (DCT) is proposed. A specific property of the designed method is its ability to predict compressed image quality with appropriately high accuracy using a limited number of analyzed blocks. This accelerates prediction of lossy compression quality substantially. The method is originally proposed for JPEG with uniform quantization and then generalized for other, advanced, DCT based coders AGU and ADCT

Peculiarities of 3D Compression of Noisy Multichannel Images

International audienceThe paper addresses practical aspects of multichannel images compression. Color and multichannel images are commonly characterized by high degree of component correlation. This property can be exploited in 3D-compression techniques for achieving high compression ratio. The main goal of this paper is to show that 3D-compression on basis of discrete cosine (DCT) transform offers some advantages in comparison to 2D application of DCT based coders

Peculiarities of 3D Compression of Noisy Multichannel Images

International audienceThe paper addresses practical aspects of multichannel images compression. Color and multichannel images are commonly characterized by high degree of component correlation. This property can be exploited in 3D-compression techniques for achieving high compression ratio. The main goal of this paper is to show that 3D-compression on basis of discrete cosine (DCT) transform offers some advantages in comparison to 2D application of DCT based coders

Lossy Compression of Landsat Multispectral Images

International audienceWe consider practical aspects of lossy compression with application to multispectral images provided by Landsat sensor. Two facts are taken into account: 1) the inherent noise presence and its properties; 2) rather high degree of component correlation. These properties in different degree are used in 2D and 3D lossy compression. Comparison of the suggested approaches has been carried out for some compression ratios. It is demonstrated that 3D-compression using techniques based on discrete cosine transform (DCT) can provide some benefits but only under condition of proper grouping of sub-band images

VST-based Lossy Compression of Hyperspectral Data for New Generation Sensors

International audienceThis paper addresses lossy compression of hyperspectral images acquired by sensors of new generation for which signal-dependent component of the noise is prevailing compared to the noise-independent component. First, for sub-band (component-wise) compression, it is shown that there can exist an optimal operation point (OOP) for which MSE between compressed and noise-free image is minimal, i.e., maximal noise filtering effect is observed. This OOP can be observed for two approaches to lossy compression where the first one presumes direct application of a coder to original data and the second approach deals with applying direct and inverse variance stabilizing transform (VST). Second, it is demonstrated that the second approach is preferable since it usually provides slightly smaller MSE and slightly larger compression ratio (CR) in OOP. One more advantage of the second approach is that the coder parameter that controls CR can be set fixed for all sub-band images. Moreover, CR can be considerably (approximately twice) increased if sub-band images after VST are grouped and lossy compression is applied to a first sub-band image in a group and to "difference" images obtained for this group. The proposed approach is tested for Hyperion hyperspectral images and shown to provide CR about 15 for data compression in the neighborhood of OOP

Prediction of Compression Ratio for DCT-Based Coders With Application to Remote Sensing Images

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