XXII ISPRS Congress 2012: Technical Commission VIII

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

Bibliographic data

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

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 TEMPORAL INDICES DATA FOR SPECIFIC CROP DISCRIMINATION USING FUZZY BASED NOISE CLASSIFIER Vijaya Musande*', Anil Kumar" , Karbhari Kale^ and P. S. Roy” ? Jawaharlal Nehru Engineering College, Aurangabad ^ Indian Institute of Remote Sensing, Dehradun * Dept. of CS & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad KEYWORDS: Indices, Fuzzy Error Matrix (FERM), Noise Classifier (NC). ABSTRACT: Evaluation of fuzzy based classifier to identify and map a specific crop using multi-spectral and time series data spanning over one growing season. The temporal data is pre-processed with respect to geo-registration and five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil- Adjusted Vegetation Index) and TVI (Triangular Vegetation Index). The noise classifier (NC) is evaluated in sub pixel classification approach and accuracy assessment has been carried out using fuzzy error matrix (FERM). The classification results with respect to the additional indices were compared in terms of image to image maximum classification accuracy. The overall accuracy observed in dataset 2 was 96.03% for TNDVI indices, using NC. Data used for this study was AWIFS for soft classification and LISS-III data for soft testing generated from Resourcesat-1(IRS-P6) satellite. The research indicates that appropriately used indices can incorporate temporal variations while extracting specific crop of interest with soft computing techniques for images having coarser spatial and temporal resolution remote sensing data. 1. INTRODUCTION Time series of acquired multispectral image represent characteristics of a landscape and each element represented has a particular spectral response, which allows the researcher to get highly relevant information to make decisions without going to the field. Since objects including vegetation, have their unique spectral features (reflectance or emission response), they can be identified from remote sensing imagery according to their unique spatial characteristics. The strong contrast of absorption and scattering of the red and near infrared bands can be combined into different quantitative indices of vegetation conditions. The time series of such vegetation indices observed over a period can help in further classification of the vegetation as crop and other type of vegetation. Classification techniques for grouping cluster and finding substructure in data needs to be robust. By robustness we mean that the performance of an algorithm should not be affected significantly by small deviations from the assumed model and it should not deteriorate drastically due to noise and outliers. Robust statistics can be related to the concept of membership functions in fuzzy set theory or possibility distributions in possibility theory. This might explain the claim made by the proponents of fuzzy set theory that a fuzzy approach is more tolerant to variations and noise in the input data when compared with a crisp approach. The immensely popular k-Means is a partitioning procedure that partitions data based on the minimization of a least squares type. The fuzzy derivative of k-Means known as Fuzzy c- Means (FCM) is based on a least squares functional; it is susceptible to outliers in the data. The performance of FCM is known to degrade drastically when the data set is noisy. This is similar to least square (LS) regression where the presence of a single outlier is enough to throw off the regression estimates. The need has therefore been to develop robust clustering algorithms within the framework of fuzzy c-means (FCM) (primarily because of FCM's simplistic iterative scheme and good convergence properties). The usual FCM minimization constraints are relaxed to make the resulting algorithm robust. The possibilistic c-means (PCM) algorithm was developed to provide information on the relationship between vectors within a cluster. Instead of the usual probabilistic memberships as calculated by FCM, PCM provides an index that quantifies the uniqueness of a data vector as belonging to a cluster. This is also shown to impart a robust property to the procedure in the sense that noise points are less unique in good clusters. Another effective clustering technique based on FCM is the noise classifier (NC) algorithm which uses a conceptual class called the noise classifier to group together outliers in the data. AII data vectors are assumed to be a constant distance, called the noise distance, away from the noise cluster. The presence of the noise cluster allows outliers to have arbitrarily small memberships in good clusters (Banerjee and Davé 2005). Till date many researchers in remote sensing field have applied time series indices to study cropping pattern. Tingting and Chuang, 2010, used the time-series NDVI to identify common vegetation types or cropping patterns. They applied principal component analysis, linear spectral un-mixing method and support vector machine to classify cropland. Panda et al., 2010, has studied four widely used spectral indices to investigate corn crop yield. Back Propagation Neural Network (BPNN) model was developed to test the efficiency of four vegetation indices in corn crop yield production. Yang et al. 2009, has given a new vegetation index which is robust to low vegetation and sensitive to high vegetation and has potential to be an alternative to NDVI for crop condition monitoring. Te-Ming et al., 2009, has proposed new vegetation index by integrating with a Fast Intensity-Hue-Saturation (FIHS) for high resolution imagery which can extract and enhance green vegetation an alternative to NDVI. Wardlow and Egbert, 2008, used a hierarchical crop mapping to classify multi-temporal NDVI data. Linlin and Huadong, 2008, presented a simple phenology model to identify wheat crop using curve fitting procedure. Lucas et al., 2007, studied the use of time-series of Landsat sensor data using decision rules based on fuzzy logic to discriminate vegetation type. They found that the rule-based classification gave a good representation of the distribution of habitats and agricultural land. Sakamoto et al, 2005, developed a new method for remotely determining phenological stages of paddy rice. As for the filtering, they adopted wavelet and Fourier transforms. Three types of mother wavelet (Daubechies, Symlet and Coiflet) were used. As the result of validation, it was observed

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