Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: INTERACTIVE SESSION 1 IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: ASSESSMENT OF NOISE VARIANCE AND INFORMATION CONTENT OF MULTI-/HYPER-SPECTRAL IMAGERY. Bruno Aiazzi, Luciano Alparone, Alessandro Barducci, Stefano Baronti, Ivan Pippi

Document type:: Monograph

Structure type:: Chapter

■ 1 June, 1999 International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3^J June, 1999 167 edener Verfahren elliten-Bilddaten. ition, No. 5, pp. ASSESSMENT OF NOISE VARIANCE AND INFORMATION CONTENT OF MULTI-/HYPER-SPECTRAL IMAGERY Bruno Aiazzi 1 , Luciano Alparone', Alessandro Barducci 1 , Stefano Baronti 1 , Ivan Pippi 1 1 “Nello Carrara” Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, 50127 Firenze, Italy, baronti@iroe.fi.cnr.it -’Department of Electronic Engineering, University of Firenze, Via S. Marta, 3, 50139 Firenze, Italy, alparone@lci.die.unifi.it KEYWORDS: Multispectral Images, SNR, Parametric Estimation, Bit Planes, Lossless Compression, Entropy Modelling. ABSTRACT This work focuses on reliably estimating the information conveyed to a user by multi-spectral and hyper-spectral image data. The goal is establishing the extent to which an increase in spectral resolution can increase the amount of usable information. Actually, a trade-off exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. After reporting about some methods developed for automatically estimating the variance of the noise introduced by multi-spectral imagers, an original and effective data de-correlation algorithm designed for lossless compression of multi/hyper-spectral data is reviewed. Data compression can be adopted to measure the useful information content of multi-spectral data. In fact, the bit rate achieved by the compression process takes into account both the entropy the so called “observation” noise (i.e. information regarded as statistical uncertainty, but whose relevance to a user is zero), and of the intrinsic information of hypothetically noise-free data. By defining a suitable model, once the standard deviation of the observation noise has been preliminarily estimated, the code rate may be utilized to yield an estimate of the true information content of the multi-spectral source, that is of one band of the multi-spectral image arranged in a causal sequence in which the previous bands are known. Results show that the information content of multi-spectral Landsat TM images is superior to that of hyper-spectral AVIRIS images, notwithstanding the latter are recorded with a 12 hit word length vs. the 8 bit of the former. 1. INTRODUCTION Estimating noise and quantifying information are two tasks of image analysis. Whereas several methods exist for assessments of signal-to-noise ratio (SNR), e.g. for filtering (Aiazzi, 1998a, 1998b), actually the latter is still an open problem. Accurate es timates of the entropy rate of an image source can only be ob tained provided that data are uncorrelated. As a consequence, data de-correlation must be considered in order to suppress or, at least, largely reduce the correlation existing in natural images. When multi-spectral images are concerned, de-correlation algo rithms should take into account not only their spatial but also their spectral correlation, to avoid over-estimating entropy (Amavut, 1998; Memon, 1994; Roger, 1996a; Ryan, 1997; Wang, 1995). Actually, the entropy rate is a measure of statistical information, that is of uncertainty of the source. Thus, any observation noise introduced by the imaging sensor will result in an increase of the entropy rate, without a corresponding enhancement of the (us able) information content. Therefore, an estimation of the noise must be preliminarily carried out in order to quantify its contri bution to the overall source entropy rate. If we assume that the noise is additive, on homogeneous areas the variance of the observed signal will be equal to the variance of the noise. This method, very simple indeed, suffers from being supervised and from needing some knowledge about the presence and location of homogeneous regions. It is possible, however, to devise some quantities that are related to the noise of the data, are based on local statistics and need no a-priori knowledge. A viable approach refers to the bit-plane representation of bit-map images. An image having L-bit word length is partitioned into L 1-bit planes. Bit planes corresponding to bits that are more significant exhibit a larger spatial correlation. By defining local measurements on the bit-planes (average length of runs of zeroes and ones, average difference between each pixel and its neigh bours) it is possible to decide whether a bit-plane exhibits spatial variations that entirely depend on the noise, or not. A different approach consists of calculating the square root of local variance on a sliding window of suitable size. The local standard devia tion exhibits a unimodal distribution irrespective of the noisiness. In fact, the presence of signal having nonzero variance tends to spread the histogram towards high values, thereby increasing its mean, but without affecting its mode. Therefore, the real valued mode, which can be extrapolated from a smoothed version of the histogram, will yield an estimate of the noise standard deviation. Unlike the former, the latter method may be generalized to signal- dependent noise, i.e. including as further parameter an exponent ruling the dependence on the signal of the additive noise contribu tion. Thus, histograms will become two-dimensional, i.e. scatter- plots, and both parameters will be estimated (Aiazzi, 1999a). The de-correlation algorithm (Aiazzi 1999c) consists of sub tracting from each pixel its space/spectral-varying prediction. Context-based classification of prediction errors is also included in order to further improve the de-correlation. Prediction for a pixel is obtained from thefuzzy-switching of a set of linear regres sion predictors. Pixels both on the current band and on previously encoded bands may be used to define a causal neighbourhood. The coefficients of each predictor are calculated so as to minimize the mean-squared error for those pixels whose intensity level pat terns lying on the causal neighbourhood, belong in a fuzzy sense to a predefined cluster. Size and shape of the causal neighbour hood and number of predictors may be chosen by the user and determine the trade-off between coding performance and compu tational cost. The method exhibits impressive results, thanks to the skill of predictors in fitting multi-spectral data patterns, re gardless of differences in sensor responses. Once the standard deviation of the observation noise, supposed to be independent of the signal, has been measured, the bit rate produced by the proposed reversible encoder will be utilized to yield an estimate of the true information content of the multi spectral source. Experimental results demonstrate that the infor mation content of multi-spectral Landsat TM images is superior to that of hyper-spectral images, notwithstanding the latter are recorded with a 12 bit word length vs. the 8 bit of the former.

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