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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-^ June, 1999 173 (b) Fig. 7. 512 x 512 details from (a) Band 1 and (b) Band 5 of the test Landsat TM image of Trento, in Italy. Fig. 8. Band 31 of AVIRIS Cuprite Mine ’89 test image. A 7 correction of 2.5 was applied for displaying purposes. AVIRIS test data are a subset of 30 (12 to 41) from the 224 bands of the 1989 image of the Cuprite Mine test site, having 614 x 512 spatial size and 12 bit word length, one band of which is portrayed in Fig. 8 as a sample of the data set. Fig. 9 reports results obtained for 30 bands (12 to 41), each predicted from a couple of previous bands. The interval of bands considered com prises two different trends. In the former (bands 12 to 29), a decreasing noisiness appears, together with a larger and larger spectral predictability, since the inter-band coding scheme pro duces decaying bit rates. Between band 19 to 29 the plots of r and h u turn out to be swapped, resulting in an undefined multi- spectral entropy h s , which is set to zero. According to the model proposed, h s = 0 indicates that the bands are spectrally over sampled, i.e. that the spectral resolution is lower than the 10 nm wavelength sampling step. Thus, one band can be exactly pre dicted from the previous ones, apart from the noise, resulting in zero information. A way to obtain nonzero information would be to discard e.g. one band every two. An abrupt change in spec tral behaviour occurs at band 30: the last bands (30 to 41) are more difficult to be spectrally predicted, as proven by the large amounts of information. Notice that the plots of SNR and h s in Fig. 9(a) and (b), respectively, are almost opposite to each other, thus indicating that spectral oversampling may lead to an SNR improvement, which is not an indicator of spectral information. Band O u ~ 2 O u SNR (dB) r{k) h u (k) hs(k) TM-1 1.39 1.93 34.5 3.63 3.33 2.85 TM-2 0.67 0.45 40.8 1.96 1.46 1.46 TM-3 0.73 0.53 40.1 2.78 1.59 2.62 TM-4 5.08 25.8 23.2 4.44 4.38 2.61 TM-5 4.43 19.6 24.4 4.19 4.18 1.10 TM-7 1.58 2.50 33.4 3.44 2.70 3.12 Table 2. Noise parameters (a u , &u and SNR) and information parameters (r(k), h u (k) and h s (k), in bit/pel) mea sured on the six 30 m bands of the test TM image. As expected, the SNR is larger in the visible than in the infrared wavelengths; thus, notwithstanding the larger code rates of the latter, the former are slightly more informative, on the average. Bands 2 and 5 are the least informative, in the sense that they may be (bidirectionally) predicted to a larger extent than the others. 6. CONCLUDING REMARKS A number of fully automatic methods have been proposed to identify and assess noise models from observed images. An extremely powerful scheme for lossless compression of multi- spectral images is reviewed as providing bit rates very close to the entropy rate of the image regarded as an information source. From the code rate and the estimated noise variance, a model was suggested to assess, or better to upper bound, the amount of usable information, i.e. the one not due to noise, of multi-/hyper- spectral images. Preliminary results indicate that bands coarsely sampling wavelengths (e.g. Lansat TM) convey more informa tion to a user than finely sampling bands (e.g. AVIRIS). In the latter case, depending on the characteristics of spectral resolution and on the 10 nm sampling step, spectral oversampling may oc cur which would explain the diminished amount of information per wavelength unit, although the SNR follows opposite trends.

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