XXII ISPRS Congress 2012: Technical Commission VII

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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

Le . pt IA edd NG ed MM } ; E (a) (b) (c) Figure 4. The fractional abundance derived from spatial interpolation-based spectral unmixing, where magnification factor was set to four (a) water, (b) bare soil / deciduous tree and (c) evergreen tree (a) (b) (c) Figure 5. The fractional abundance derived from FCLS (a) water, (5) bare soil / deciduous tree and (c) evergreen tree (a) Figure 6. The fractional abundance derived from hard classification (a) water, (b) bare soil / deciduous tree and (c) evergreen tree As the ASTER image was unmixed, it is necessary to evaluate accuracy of different unmixing algorithms. Root mean square error (RMSE) was used as the error index using the following formula: (1) where R; is the RMSE value of ith endmember, Pi is the real fractional abundance of ith endmember in jth pixel, P is the estimated fractional abundance of ith endmember in jth pixel. In addition, to evaluate the total RMSE value of all endmembers, RMSE values of all endmembers should averaged as the average RMSE. Then RMSE values of the proposed spatial interpolation (SI) algorithm and other algorithms were listed in Table 1. 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 RMSE Algorithm Evergreen | Deciduous tree / Water [Average tree bare soil SI (M=2) 0.0599 0.1807 0.1743 | 0.1385 SI (M=3) 0.0539 0.1136 0.1084 | 0.0920 SI (M=4) 0.0499 0.0826 0.0768 | 0.0698 SI (M=5) 0.0542 0.1130 0.1082 | 0.0918 SI (M=6) 0.0583 0.1178 0.1119 | 0.0960 FCLS 0.0844 0.2025 0.1705 | 0.1525 MESMA 0.1560 0.2408 0.2904 | 0.2291 Hardy 0.0819 0.1965 0.1905 | 0.1563 classification Table 1. RMSE values of different spectral unmixing algorithms, where M denotes the magnification factor From Table 1, it is found that spatial interpolation-based spectral unmixing has higher accuracy than that of the MESMA, FCLS and hard classification, as the average RMSE values show. However, the performance of the proposed algorithm depends on the parameter, the magnification factor. The unmixing error reaches the bottom line when the magnification factor is equal to four, whereas other values of the factor resulted in different average RMSE values with large variation. Nevertheless, the proposed algorithm has higher accuracy than that of the LSU whatever the magnification factor is chosen. The result is interesting because only using a simple spatial interpolation is efficient to retrieve endmember proportion from mixed pixels, as this study shows. Although the spatial interpolation will absolutely ignore some detail information in the required higher resolution image, it is also effective since the resolution conversion in SRSU has reduced the impact of detail information in higher resolution. Thus spatial interpolation has great potential for spectral unmixing of remotely sensed imagery as this experiment shows. 5. CONCLUSION SRSU is a new arising method for spectral unmixing of remotely sensed imagery, and has shown good performance in previous studies. However, training database in SRSU is a big obstacle for convenient use of SRSU. In this study, spatial interpolation, much easier to implement, is used as an alternative approach to achieve super resolution reconstruction. An experiment using ASTER image shows that this simplified version of SRSU also performs better than that of linear spectral unmixing and hard classification, since the RMSE of the proposed algorithm is smaller than that of the linear spectral unmixing. Among the different values of magnification factor, it was discovered that when the image is resampled to four times of the original size, the proposed algorithm shows the highest spectral unmixing accuracy. This study opens a door to convenient use of SRSU, which will make more scholars to recognize the importance and power of SRSU. Admittedly, some important issues, such as the optimal spatial interpolation method and magnification factor, should be considered carefully in future studies. As a result, the land cover mapping from remotely sensed imagery will be more accurate by use of the improved SISU.

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