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

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 SPATIAL INTERPOLATION AS A TOOL FOR SPECTRAL UNMIXING OF REMOTELY SENSED IMAGES Li Xi *, Chen Xiaoling State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China - li rs@163.com Commission VII, WG VII/4 KEY WORDS: Land Cover, Mapping, Analysis, Algorithms, Image, Spectral ABSTRACT: Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping. This study makes use of spatial interpolation as an alternative approach to achieve super resolution reconstruction in SRSU. An ASTER image with three spectral bands was used as the test data. The algorithm is evaluated using root mean square error (RMSE) compared with linear spectral unmixing and hard classification. The result shows that the proposed algorithm has higher unmixing accuracy than those of the other comparative algorithms, and it is proved as an efficient and convenient spectral unmixing tool of remotely sensed imagery. 1. INTRODUCTION Classification of remotely sensed images has long been an issue in remote sensing community, since it is indispensable for land cover mapping which plays an important role in global change studies. Conventional technique of image classification, also called hard classification, labels each remote sensing pixel with a single class, and mixed pixels which comprise two or more classes are not considered. Thus, the problem of mixed pixels hinders precise land cover mapping when using hard classification, since all mixed pixels are classified into single classes. To overcome the problem of mixed pixels, spectral unmixing was proposed to decompose mixed pixels into several classes and corresponding abundances, which reflect the land cover types more accurately. Typical spectral unmixing algorithms can be categorized into two types, linear spectral unmixing (Roberts, Gardner et al, 1998; Heinz and Chang, 2001) and nonlinear spectral unmixing (Huang and Townshend, 2003; Liu, Seto et al., 2004; Lee and Lathrop, 2006). In linear spectral unmixing, the spectrum of a mixed pixel was viewed as the weighted summation of spectra of different endmembers in the pixel, whereas the weight is the proportion of the endmember in the pixel (Heinz and Chang, 2001). In nonlinear spectral unmixing, the mechanism of the spectral mixture mechanism is more nonlinear or unknown, and a variety of algorithms, including support vector machine model (Brown, Gunn et al., 1999), neural network model (Liu, Seto et al., 2004; Lee and Lathrop, 2006) and physical model (Kimes and Nelson, 1998), were proposed to retrieve the proportions of different endmembers. Both existing linear and nonlinear spectral unmixing focus on how to utilize the spectral information of pixels, and the spatial neighbourhood information is always ignored although a small number of algorithms attempt to utilize the spatial information (Roessner, Segl et al., 2001). Recently, a new model named super resolution based spectral unmixing (SRSU) was proposed to utilize the spatial information in spectral unmixing (Li, Tian et al., 2011). This model is totally different from the conventional models, the * Corresponding author. linear and nonlinear models, since it focuses on spatial dimension rather than spectral dimension. SRSU has shown good performance in land cover mapping, and is a potential powerful approach in the spectral unmixing family. However, this model is developed in primary status that an image database providing prior knowledge is required, and this perquisite makes the model inconvenient for use. This study aims to propose a simple way to implement SRSU, and evaluates its performance compared with conventional spectral unmixing. 2. BACKGROUND In this section, the previously developed super resolution-based spectral unmixing (SRSU) model is introduced. The essence of SRSU is that downscaling a remotely sensed image helps to reduce spectral mixing in the image and hence the downscaled (super resolution) image can be processed with a hard classification method to produce the land cover proportion map in the original resolution. The SRSU has following steps as following steps 1) Use a super resolution technique to downscale a remotely sensed image to a super resolution image; 2) Classify the super resolution image with a hard classification method; 3) Convert the super resolution classification map to original resolution to produce a set of proportion maps for different endmembers, which are the product of spectral unmixing. The key technique used in SRSU is super resolution reconstruction, which refers to reconstruct a higher resolution image from the original image. Since this reconstruction is ill- posed from a mathematical perspective, prior information should be introduced to regulate this process. In super resolution reconstruction, prior information is provide by a training database, which reflects the relationship of low resolution images and their corresponding high resolution images in natural scenes. A number of algorithms have been proposed to merge the training database into the super resolution reconstruction (Freeman et al., 2002; Kim and Kwon, 2010), but each algorithm is complex.

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