XXII ISPRS Congress 2012: Technical Commission VII

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:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

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

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

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:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: FOREST RESOURCES STUDY IN MONGOLIA USING ADVANCED SPATIAL TECHNOLOGIES D. Amarsaikhan, M. Saandar, V. Battsengel, Sh. Amarjargal

Document type:: Multivolume work

Structure type:: Chapter

1gust 1dsat atial ation m, a omy test ND [M+ ction 0. In tural other d on total order have were cally CPs with the r the arest MS e to 1sed very adar the the ). In such Iters the rest ach the well tput n of the ning s to … In sed. and g an vere "nce x to specified neighbour (ENVI 1999). The contrast measure indicates how most elements do not lie on the main diagonal, whereas, the dissimilarity measure indicates how different the elements of the co-occurrence matrix are from each other (Lee et al. 2004). By applying these measures, initially 12 features have been derived, but after thorough checking of each individual feature only 2 features, including the result of the contrast measure applied to the green band and the result of the dissimilarity measure applied to the near infrared band of Landsat, were selected. To define the sites for the training signature selection from the images, two to three areas of interest (AOT) representing the selected classes (i.e., coniferous forest, deciduous forest, grassland, light soil, dark soil and water) have been selected. As the data sources included both optical and SAR features, the fused images were very useful for the determination of the homogeneous AOI as well as for the initial intelligent guess of the training sites. The separability of the training signatures was firstly checked in feature space and then evaluated using transformed divergence (Mather and Koch 2010). After the investigation, the samples that demonstrated the greatest separability were chosen to form the final signatures. The final signatures included about 563-992 pixels. For the classification, the following feature combinations were used: 1. The original spectral bands of the multitemporal Landsat data. 2. PALSAR and original bands of the multitemporal Landsat data. 3. Multiple bands including the original PALSAR and Landsat images as well as two other derivative bands obtained from texture measures. For the actual classification, a standard maximum likelihood classification has been used assuming that the training samples have the Gaussian distribution. The maximum likelihood classification is the most widely used statistical classification technique, because a pixel classified by this method has the maximum probability of correct assignment (Erbek ef al. 2004). To increase the reliability of the classification, to the initially classified images, a fuzzy convolution with a 3x3 size window was applied. The fuzzy convolution creates a thematic layer by calculating the total weighted inverse distance of all the classes in a determined window of pixels and assigning the centre pixel the class with the largest total inverse distance summed over the entire set of fuzzy classification layers, i.e. classes with a very small distance value will remain unchanged while the classes with higher distance values might change to a neighboring value if there are a sufficient number of neighboring pixels with class values and small corresponding distance values (ERDAS 1999). The visual inspection of the fuzzy convolved images indicated that there are some improvements on the borders of the neighboring classes that significantly influence the separation of the decision boundaries in multidimensional feature space. The final classified images are shown in figure 3(a-c). As seen from figure 3(a-c), the classification result of the Landsat image gives the worst result, because there are high overlaps among grassland and forest classes. However, these overlaps decrease on other images for the classification of which SAR and optical bands as well as other derivative features have been used. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia For the accuracy assessment of the classification results, the overall performance has been used. This approach creates a confusion matrix in which reference pixels are compared with the classified pixels and as a result an accuracy report is generated indicating the percentages of the overall accuracy (Richards and Jia, 1999). As ground truth information, different AOIs containing 5,751 purest pixels have been selected. AOIs were selected on a principle that more pixels to be selected for the evaluation of the larger classes such as grassland and coniferous forest than the smaller classes such as deciduous forest and dark soil. The overall classification accuracies for the selected classes were 74.35%, 79.18% and 83.09% for the original Landsat bands, the combined features and multiple bands, respectively. As could be seen from the overall classification results, although the combined use of optical and SAR data sets produced a better result than the single source image, it is still very difficult to obtain a reliable forest map by the use of the standard technique, specifically on decision boundaries of the statistically overlapping classes. 5. THE REFINED MAXIMUM LIKELIHOOD CLASSIFICATION For several decades, single-source multispectral data sets have been effectively used for a land cover mapping. Unlike single- source data, multisource data sets have proved to offer better potential for discriminating between different land cover types (Amarsaikhan et al. 2012). Generally, it is very important to design a suitable image processing procedure in order to successfully classify any RS data into a number of class labels. The effective use of different features derived from different sources and the selection of a reliable classification technique can be a key significance for the improvement of classification accuracy (Lu and Weng, 2007). In this study, for the classification of land cover types, a refined statistical maximum likelihood classification algorithm has been constructed. As the features, multiple bands that include the original Landsat and PALSAR images as well as two other derivative bands obtained from texture measures have been used. Unlike the traditional method, the constructed classification algorithm uses spectral and spatial thresholds defined from the contextual knowledge. The contextual knowledge was defined on the basis of the spectral variations of the land surface features on the fused images as well as the texture information delineated on the dissimilarity image. For determination of the spectral thresholds, maximum and minimum values of the selected training signatures and the pixels falling within 1.0 standard deviation were compared, and the latter was selected. It is clear that a spectral classifier will be ineffective if applied to the statistically overlapping classes, because they have very similar spectral characteristics. For such spectrally mixed classes, classification accuracies should be improved if the spatial properties of the classes of objects could be incorporated into the classification criteria. The idea of the spatial threshold is that it uses a polygon boundary to separate the overlapping classes and only the pixels falling within the threshold boundary are used for the classification. In that case, the likelihood of the pixels to be correctly classified will significantly increase, because the pixels belonging to the class that overlaps with the class to be classified using the threshold

Access restriction

Copyright

fullscreen: Technical Commission VII (B7)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation