XXII ISPRS Congress 2012: Technical Commission VII

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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:: 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/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: SPATIAL INTERPOLATION AS A TOOL FOR SPECTRAL UNMIXING OF REMOTELY SENSED IMAGES Li Xi, Chen Xiaoling

Document type:: Multivolume work

Structure type:: Chapter

3. METHODOLOGY In SRSU, super resolution reconstruction aims to improve the spatial resolution of images, therefore spatial interpolation may be an alternative way to achieve this purpose, although a lot of detail information in the higher resolution image can not be restored. At a first glance, simple interpolation of an image will not perform well in downscaling an image. Fortunately in SRSU, the super resolution classification map will be converted into the original resolution, and this conversion may alleviate the impact of downscaling error on the SRSU. Therefore, it is interesting to evaluate the role of spatial interpolation instead of super resolution reconstruction in SRSU, because this alternative technique is particularly simple. The spatial interpolation based spectral unmixing (SISU) has similar steps as the SRSU, the only difference is replacing the super resolution reconstruction with spatial interpolation. The SISU has following steps as 1) Use spatial interpolation to downscale a remotely sensed image to higher resolution. 2) Classify the obtained higher resolution image. 3) Convert the classification map into the proportion maps of different endmembers in original resolution, and the final spectral unmixing result is derived. The algorithm is also illustrated in Figure 1. In this study, a bilinear interpolation method was employed for spatial interpolation, since it is the easiest interpolation method to achieve image downscaling. / Remotely 7 Sensed Imagery 7 / / | Spatial Interpolation ¥ / Higher Resolution / of Imagery / Y Hard Classification + p Classification Map / / * ; Resolution Coversion i y Abundance Map in / / m : Z / the original resolution — / Figure 1. The flowchart of spatial interpolation-based spectral unmixing 4. EXPERIMENTS 4.1 Study area and data The study area and data are the same as the initial work of super resolution-based spectral unmixing (Li, Tian et al., 2011). The study area was located in a lake in Massachusetts, U.S.A. An 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 ASTER image patch was selected as the study material for spectral unmixing. To make the image comprise enough mixed pixel, the ASTER image was resampled to 30 m resolution. Finally the ASTER image has 120 X 102 pixels and three spectral bands (Figure 2). Besides, an aerial photograph with resolution of 0.5 m was used to generate the reference data. At first, the aerial photograph was visually interpreted to three types, evergreen tree, bare soil / deciduous tree and water. Then the high resolution interpreted map was converted into 30 m resolution, which was viewed as the reference data for the spectral unmixing result, as shown in Figure 3. Figure 2. The ASTER image (Ban 2) for spectral unmixing, at 42925: 32"N,, 72916" 39 N/ 4.20 Procedure The procedure of the experiments has following steps: 1) Manually select sample of the three land cover types from the ASTER image. 2) The ASTER image processed for spectral unmixing, using the endmember spectrum in step 1. The image magnification time varies from 2 to 6, thus there are five results for the spectral unmixing. 3) Linear spectral unmixing method was also used to generate spectral unmixing result to compare with the proposed algorithm. The LSU was achieved with fully constrained least square (FCLS) algorithm (Heinz and Chang, 2001) and multiple endmember spectral mixture analysis (MESMA) (Roberts, Gardner et al., 1998), which are two kinds of widely used spectral unmixing algorithms. 4) Hard classification was used as another comparative algorithm. And a support vector machine was employed as the classifier. 4.3 Results Using the proposed spectral unmixing algorithm and three comparative algorithms, the ASTER image was finally unmixed as Figures 4-6 show. For the proposed algorithm, only the result with magnification factor of four is shown. Since MESMA has resulted in some negative values, result of MESMA is inconvenient to show. (a) (b) Figure 3. The fractional abundance map of reference data (a) water, (5) bare soil / deciduous tree and (c) evergreen tree

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