期刊缩写       REMOTE SENS LETT

期刊全称       Remote Sensing Letters 《遥感通信》

期刊ISSN      2150-704X

2013-2014最新影响因子     1.427

期刊官方网站       http://www.tandfonline.com/toc/trsl20/current#

              http://pubget.com/paper/21673829/the-effect-of-input-data-transformations-on-object-based-image-analysis

期刊投稿网址      

通讯方式      

涉及的研究方向    REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

出版国家       ENGLAND

出版周期      

出版年份       2010

年文章数       127

 

 

The effect of input data transformations on object-based image analysis.

 

Christopher D Lippitt, Lloyd Coulter, Mary Freeman, Jeffrey Lamantia-Bishop, Wyson Pang, and Douglas A Stow

Remote Sensing Letters 3(1):21 (2012) PMID 21673829

 

The effect of using spectral transform images as input data on segmentation quality and its potential effect on products generated by object-based image analysis are explored in the context of land cover classification in Accra, Ghana. Five image data transformations are compared to untransformed spectral bands in terms of their effect on segmentation quality and final product accuracy. The relationship between segmentation quality and product accuracy is also briefly explored. Results suggest that input data transformations can aid in the delineation of landscape objects by image segmentation, but the effect is idiosyncratic to the transformation and object of interest.

 

 

Agreement between monthly land rainfall estimates from TRMM-PR and gauge-based observations over South Asia

 

ABSTRACT There is a demand for reliable rainfall data-set over the South Asia region covering both land and ocean for model validation/development and various applications. For satellite rainfall estimates (SREs), the algorithm development groups also need validation information on SRE. The Tropical Rainfall Measuring Mission (TRMM) project has produced recently improved version 7 (V7) rainfall data-sets. Version 6 (V6) and V7 of 3A25, the surface rainfall products derived from TRMM precipitation radar (PR), are compared with gauge-based observations at 0.5° latitude/longitude resolution for the period of 1998–2007 over the South Asian land region. Both 3A25V7 and 3A25V6 represent the mean rainfall distribution patterns reasonably well. However, 3A25 products overestimate rainfall over the Indonesian region compared to gauge-based data. For some parts of South Asia, SREs show considerable difference in the magnitude of coefficient of variation compared to gauge-based information. At seasonal scale, a contrasting feature in bias over India during the pre-monsoon and monsoon seasons is noticed from both the versions of 3A25 data-set. In general, 3A25 rainfall data-sets are able to capture the interannual variability of rainfall over South Asia. The frequency distribution of monthly rainfall rate reveals that 3A25 products marginally underestimate rainfall below 10 mm day?1 and overestimate higher rainfall rate compared to gauge-based data. Overall, 3A25V7 product is marginally better than its previous version (3A25V6) over the South Asian land region.

 

 

Mapping urban land cover types using object-based multiple endmember spectral mixture analysis

ABSTRACT Spectral mixture analysis has been frequently applied in various fields to solve the mixed pixel problem in remote sensing. So far, all the research in mixture analysis has focused on the sub-pixel analysis, i.e., selecting endmembers and conducting mixture analysis at the pixel level. Research efforts in mixture analysis at the object level are very scarce, even though the object-based image analysis (OBIA) techniques have been well developed. In this study, we examined the applicability of object-based mixture analysis in an urban environment using a Landsat Thematic Mapper image. Informative and accurate object-based fraction maps (vegetation, impervious surface, and water) were produced by combining the OBIA and multiple endmember spectral mixture analysis (MESMA) techniques. A new approach to identifying the spectral representatives of a specific class for MESMA was developed. The accuracy of the object-based fraction maps were assessed using manual interpretation results of a 1-m digital aerial photograph. Object-based mixture analysis produced a higher accuracy than traditional pixel-based mixture analysis. This work illustrates the potential of object-based mixture analysis of moderate spatial resolution imagery in mapping heterogeneous urban environments.