TY - JOUR
T1 - Enhanced Subpixel Mapping with Spatial Distribution Patterns of Geographical Objects
AU - Ge, Yong
AU - Chen, Yuehong
AU - Stein, Alfred
AU - Li, Sanping
AU - Hu, Jianlong
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 41471296 and in part by the Key Technologies Research and Development Program of China under Grant 2012BAH33B01.
Publisher Copyright:
© 1980-2012 IEEE.
PY - 2016/4
Y1 - 2016/4
N2 - This paper proposes spatial distribution pattern-based subpixel mapping (SPMS) as a novel subpixel mapping (SPM) strategy. It separately considers spatial distribution patterns of different types of geographical objects. Initially, it classifies geographical objects into areal, linear, and point patterns according to their spatially geometric characteristics. For the different patterns, SPMS uses the vectorial boundary-based SPM algorithm with the spatial dependence assumption to deal with areal objects, the linear template matching-based SPM algorithm for linear objects, and the spatial pattern consistency matching-based SPM algorithm for point objects. The three patterns are integrated to generate a subpixel map. An artificially created image and two remotely sensed images were used to evaluate the performance of SPMS. The results were compared with a traditional hard classifier and seven existing SPM methods. The experimental results demonstrated that SPMS performed better than the hard classification and traditional SPM methods, particularly when dealing with linear and point objects.
AB - This paper proposes spatial distribution pattern-based subpixel mapping (SPMS) as a novel subpixel mapping (SPM) strategy. It separately considers spatial distribution patterns of different types of geographical objects. Initially, it classifies geographical objects into areal, linear, and point patterns according to their spatially geometric characteristics. For the different patterns, SPMS uses the vectorial boundary-based SPM algorithm with the spatial dependence assumption to deal with areal objects, the linear template matching-based SPM algorithm for linear objects, and the spatial pattern consistency matching-based SPM algorithm for point objects. The three patterns are integrated to generate a subpixel map. An artificially created image and two remotely sensed images were used to evaluate the performance of SPMS. The results were compared with a traditional hard classifier and seven existing SPM methods. The experimental results demonstrated that SPMS performed better than the hard classification and traditional SPM methods, particularly when dealing with linear and point objects.
KW - Classification
KW - Mixed pixels
KW - Remotely sensed images
KW - Spatial distribution patterns of geographical objects
KW - Subpixel mapping (SPM)
KW - 2023 OA procedure
UR - http://www.scopus.com/inward/record.url?scp=84954570284&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2015.2499790
DO - 10.1109/TGRS.2015.2499790
M3 - Article
SN - 0196-2892
VL - 54
SP - 2356
EP - 2370
JO - IEEE transactions on geoscience and remote sensing
JF - IEEE transactions on geoscience and remote sensing
IS - 4
M1 - 7383275
ER -