지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수
초록· 키워드
오류제보하기
Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called “super-resolution mapping” to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.
목차
Abstract
1. Introduction
2. Relation between spatial resolution and spatial dependence
3. Experiment of super-resolution mapping using simulated spot image
4. Conclusion
References
참고문헌 (16)
참고문헌 신청
Atkinson, P. M. / 1997 / Mapping sub-pixel boundaries from remotely sensed images, In Innovation in GIS 4 / Taylor & Francis :166~180
Atkinson, P. M. / 2008 / Super-Resolution Mapping Using the Two-Point Histogram and Multi-Source Imagery, In geoENV VI – Geostatistics for Environmental Applications / Springer :307~321
Bast in, L. / 1997 / Comparison of fuzzyc-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels / International Journal of Remote Sensing :3629~3648
Brown, M. / 2000 / Linear spectral mixture models and Support Vector Machines for Remote Sensing / IEEE Transactions on Geoscience and Remote Sensing :2346~2360
Foody, G. M. / 2004 / Sub-pixel methods in Remote Sensing, In Remote Sensing Image Analysis / Kluwer Academic Publisher :37~49
Atkinson, P. M. / 2008 / Super-Resolution Mapping Using the Two-Point Histogram and Multi-Source Imagery, In geoENV VI – Geostatistics for Environmental Applications / Springer :307~321
Bast in, L. / 1997 / Comparison of fuzzyc-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels / International Journal of Remote Sensing :3629~3648
Brown, M. / 2000 / Linear spectral mixture models and Support Vector Machines for Remote Sensing / IEEE Transactions on Geoscience and Remote Sensing :2346~2360
Foody, G. M. / 2004 / Sub-pixel methods in Remote Sensing, In Remote Sensing Image Analysis / Kluwer Academic Publisher :37~49
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UCI(KEPA) : I410-ECN-0101-2014-530-003341800