XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

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

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/6: Agriculture, Ecosystems and Bio-Diversity]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: TEMPORAL INDICES DATA FOR SPECIFIC CROP DISCRIMINATION USING FUZZY BASED NOISE CLASSIFIER Vijaya Musande, Anil Kumar, Karbhari Kale and P. S. Roy

Document type:: Multivolume work

Structure type:: Chapter

that wavelet transform performed better than the fourier transform. Kumar and Roy, 2010, has worked with add on bands in multi-spectral dataset of Worldview -2. This work has proposed class based sensor independent spectral band ratio NDVI approach for extracting crop information. Yang et al., 2008, has identified that the accuracy of surface feature recognition is improved greatly, by introducing fuzzy statistics variables into classical principal component analysis (PCA) methods on applying to the multi-spectral Landsat ETM- data for image enhancement. Kumar and Saggar, 2008, have found that possibilstic fuzzy classifier can be used for single class extraction of interest. Class based ratio data was used as input in possibilistic fuzzy classifier and water class has been identified at sub-pixel level. It was also observed from this approach that shadow pixels were not mixing with water class pixels. Acharyya et al., 2003, has studied a feature extraction method based on m-band wavelet packet frames for segmenting remotely sensed images. These wavelet features are then evaluated and selected using an efficient neuro fuzzy algorithm. The effectiveness of the methodology was demonstrated on two four-band Indian Remote Sensing satellite (IRS-1A) images containing five to six overlapping classes and a three-band SPOT image containing seven overlapping classes. Dave 1991, has introduced the concept of characterization and detection of noise in clustering. He has presented the approach which is applicable to a variety of fuzzy clustering algorithms as well as regression analysis. Dave and Krishnapuram 1997, has studied that the classical approach to clustering based on variations of the K-means or the fuzzy c-means is not robust. The alternative formulations based on noise clustering or possibilistic clustering is robust in that they can be shown to be founded on robust statistics. Banerjee and Davé 2005, proposed a scheme, called as mega-clustering algorithm is shown to be robust against outliers. Another interesting property is its ability to distinguish between true outliers and non-outliers (vectors that are neither part of any particular cluster nor can be considered true noise). Robustness is achieved by scaling down the fuzzy memberships, as generated by FCM. A lot of work has been done in the field of single class extraction through time series multi-spectral data but while going through the literature it has been identified that the effects of various band ratio indices for fuzzy noise classifier along with crop phenology has not been explored in the past. 2. INDICES AND CLASSIFICATION APPROACHES To enhance the vegetation signal in remotely sensed data and provide an approximate measure of green vegetation amount, a number of spectral vegetation indices have been proposed. By combining data from multiple bands into single values, because they correlate the biophysical characteristics of the vegetation of the land cover from the satellite spectral signals. A common practice in the remote sensing is the use of band ratio to eliminate the various albedo effects. Jordan (1969), first presented the ratio vegetation index (RVI) or simple ratio (SR). Rouse et al, 1973, further suggested the most widely used normalized difference vegetation index (NDVI) to improve identification of vegetated areas and their conditions. However, the NDVI index is saturated in high biomass and it is sensitive to a number of perturbing factors, such as atmospheric effects, cloud, soil effects, and anisotropic effects, etc. Therefore, a number of derivatives and alternatives to NDVI have been proposed in the scientific literature to address these limitations. Tucker (1979), presented a transformed normalized difference vegetation index (TNDVI) by adding a constant 0.5 to NDVI International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia and taking the square root. It always has positive values and the variances of the ratio are proportional to mean values. TNDVI indicates a slight better correlation between the amount of green biomass and that is found in a pixel (Senseman ef al. 1996). To reduce the impact to the NDVI from the soil variations in lower vegetation cover areas, Huete (1988) proposed a soil-adjusted vegetation index (SAVI) by introducing a correction factor L (Zhengwei et al., 2008). Broge and Leblanc (2000), developed triangular vegetation index (TVI), which describes the radiative energy absorbed by the pigments as a function of the relative difference between red and near-infrared reflectance in conjunction with the magnitude of reflectance in the green region, where the light absorption by chlorophyll a and b is relatively insignificant. Table 1 show different indices studied in this work. Vegetation Index Equation References Birth and Pp. McVey, Simple Ration (SR) SR = EE 1968; td Colombo et al.,2003 Rouse et Normalized al., 1973; Difference NDVI = Fair — Pred Deering et Vegetation Index Pair + Fred al. 1975; (NDVI) Huete et al., 2002 ; ; Huete and Sal Se 1 + LY Bir Fred) Liu, 1994; Vegetation Index SAVI pr Rania B (SAVI) nir * ^ red al mm Triengular TVI =0.5(120(P,ir — Pereen)) | Broge and Vegetation Index - 200(P ed 7 Preen Ter. (TVI) Transformed Tucker, Normalized 1 1979 Difference 2 nir — Pred }- 0.5 ? Vegetation Index Bair + Pred (TNDVI) Table 1: Various indices studied in this work 3. NOISE CLASSIFIER The concept of "Noise Cluster' is introduced such that noisy data points may be assigned to the noise class. The approach is developed for objective functional type (K-means or fuzzy K- means) algorithms, and its ability to detect 'good' clusters amongst noisy data is demonstrated. Clustering methods need to be robust if they are to be useful in practice. Uncertainty is imposed simultaneously with multispectral data acquisition in remote sensing. It grows and propagates in processing, transmitting and classification processes. This uncertainty affects the extracted information quality. Usually, the classification performance is evaluated by criteria such as the accuracy and reliability. These criteria cannot show the exact quality and certainty of the classification results. Unlike the correctness, no special criterion has been put forth for evaluation of the certainty and uncertainty of classification results. It follows the uncertainty problem in multispectral data classification process. Several uncertainty criteria are introduced and applied in order to evaluate the classification performance as membership value generation have been shown in equation (1) and (2). Inter where and 0 >0 The o be fot And V>0 0 >( Whei i=s and \ (19° shov Indi: cottc cottc high cottc inter Wid sens resp satel the clas: infei the TIS:

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