|
|
|
|
|
|
Sub-pixel mapping of land cover using sub-pixel swapping algorithm and topographic data |
| YU Zhoulu1, WANG Wenchao2, RONG Yi2, SHEN Zhangquan1 |
1. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China;
2. Hangzhou Federation of Land Planning and Design Consulting Co. Ltd., Hangzhou 310030, China |
|
|
|
|
Abstract When remote sensing images are used to provide information for land cover mapping, it is negatively affected by the occurrence of mixed pixels in the remote sensing images, particularly in the case of coarse spatial resolutions. Soft classification and super-resolution(sub-pixel) mapping techniques can solve this kind of problems. The pixel-swapping(PS)algorithm is a simple and efficient technique for sub-pixel mapping. However, its computation is inefficient and yields poor mapping accuracy when the super-resolution scale factor (S)is large. A possible reason for this is that it only relies on the information from the fraction images. In this study, the digital elevation model(DEM) and their derivative data are employed as supplementary information for the PS algorithm so as to improve super-resolution mapping accuracy. Some conclusions have been reached: ① The sub-pixel mapping accuracy could be improved with the assistance of the DEM even if the scale factor is large; ② The mapping accuracy by incorporating both elevation and slope information is better than that of using elevation or slope data alone; ③ Mapping accuracy is less sensitive to the number of neighbors when scale factor is large;④ The computing efficiency is improved when incorporating DEM in pixel-swapping. Thus, it is feasible to use DEM as supplemental information for sub-pixel mapping.
|
| Keywords
Hyperion dimensional reduction
IKONOS fusion
segmentation
spectral characteristics
classification
|
|
|
|
Issue Date: 04 December 2017
|
|
|
[1] Tatem A J,Lewis H G,Atkinson P M,et al.Super-resolution land cover pattern prediction using a Hopfield neural network[J].Remote Sensing of Environment,2002,79(1):1-14.
[2] Kasetkasem T,Arora M K,Varshney P K.Super-resolution land cover mapping using a Markov random field based approach[J].Remote Sensing of Environment,2005,96(3/4):302-314.
[3] Foody G M.Remote Sensing Image Analysis:Including the Spatial Domain[M].Norwell,MA:Kluwer,2004:37-49.
[4] Mertens K C,Verbeke L P C,Ducheyne E I,et al.Using genetic algorithms in sub-pixel mapping[J].International Journal of Remote Sensing,2003,24(21):4241-4247.
[5] 任 武,葛 咏.遥感影像亚像元制图方法研究进展综述[J].遥感技术与应用,2011,26(1):33-44.
Ren W,Ge Y.Progress on sub-pixel mapping methods for remotely sensed images[J].Remote Sensing Technology and Application,2011,26(1):33-44.
[6] sensed images[M]//Kemp Z.Innovations in GIS IV.London:Taylor and Francis,1997:166-180.
[7] Atkinson P M.Super-resolution target mapping from soft-classified remotely sensed imagery[C]//Proceedings of the 5th International Conference on GeoComputation.Leeds,UK:University of Leeds,2001.
[8] Atkinson P M.Sub-pixel target mapping from soft-classified,remotely sensed imagery[J].Photogrammetric Engineering and Remote Sensing,2005,71(7):839-846.
[9] Foody G M.Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution[J].International Journal of Remote Sensing,1998,19(13):2593-2599.
[10] Grossa H N,Schotta J R.Application of spectral mixture analysis and image fusion techniques for image sharpening[J].Remote Sensing of Environment,1998,63(2):85-94.
[11] Schneider W.Land use mapping with subpixel accuracy from Landsat TM image data[C]//Proceedings of 25th International Symposium,Remote Sensing and Global Environmental Change.Austria,Graz:[s.n.],1993,2:155-161.
[12] Tatem A J,Lewis H G,Atkinson P M,et al.Super-resolution target identification from remotely sensed images using a Hopfield neural network[J].IEEE Transaction on Geoscience and Remote Sensing,2001,39(4):781-796.
[13] Nguyen M Q,Atkinson P M,Lewis H G.Superresolution mapping using a Hopfield neural network with fused images[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(3):736-749.
[14] Ruiz C P,López F J A.Restoring SPOT images using PSF-derived deconvolution filters[J].International Journal of Remote Sensing,2002,23(12):2379-2391.
[15] Verhoeye J.De Wulf R.Land cover mapping at sub-pixel scales using linear optimization techniques[J].Remote Sensing of Environment,2002,79(1):96-104.
[16] Mertens K C,Verbeke L P C,Westra T,et al.Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients[J].Remote Sensing of Environment,2004,91(2):225-236.
[17] Mertens K C,De Baets B,Verbeke L P C,et al.A sub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models[J].International Journal of Remote Sensing,2006,27(15):3293-3310.
[18] Shen Z Q,Qi J G,Wang K.Modification of pixel-swapping algorithm with initialization from a sub-pixel/pixel spatial attraction model[J].Photogrammetric Engineering and Remote Sensing,2009,75(5):557-567.
[19] 沈掌泉,虞舟鲁.基于单亲遗传算法和子像元/像元空间吸引模型的亚像元制图研究[J].遥感技术与应用,2015,30(1):129-134.
Shen Z Q,Yu Z L.Sub-pixel mapping with partheno-genetic algorithm and sub-pixel/pixel spatial attraction model[J].Remote Sensing Technology and Application,2015,30(1):129-134.
[20] Muslim A M,Foody G M,Atkinson P M.Localized soft classification for super-resolution mapping of the shoreline[J].International Journal of Remote Sensing,2006,27(11):2271-2285.
[21] Thornton M W,Atkinson P M,Holland D A.Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixel-swapping[J].International Journal of Remote Sensing,2006,27(3):473-491.
[22] Nguyen M Q,Atkinson P M,Lewis H G.Superresolution mapping using a Hopfield neural network with LiDAR data[J].IEEE Geoscience and Remote Sensing Letters,2005,2(3):366-370. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
2017,
Vol. 29
