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Super Resolution Mapping Based on Spatial-Spectral Attraction Model and the New Class Allocation Approach
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Mahnaz Dastjani   , Mohammad Javad Valadan Zoej * , Mojtaba Jannati |
| Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology |
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Abstract: (1901 Views) |
| The mixed pixels influence the overall accuracy of land cover maps produced by using the remote sensing images with different spatial resolutions. In recent years, soft-then-hard super resolution mapping (STHSRM) has been proposed to solve the problem of mixed pixels. This method estimates soft attribute values for land cover classes at the subpixel scale level and then allocates classes for subpixels according to the soft attribute values. Subpixel/pixel spatial attraction model (SPSAM) calculates soft attribute values for each class at fine pixels by spatial attraction between subpixels and their neighboring pixels. UOC (Units of Class) allocates classes to subpixels in units of class. First, a visiting order for all classes is predetermined. Then, according to the visiting order, the subpixels belonging to the being visited class are determined by comparing the soft attribute values of this class. The remaining subpixels are used for the allocation of the next class. This paper proposed a new spatial-spectral attraction model to estimate the soft attribute value for each class at each subpixel. Also it presents a novel class allocation approach based on UOC technique for STHSRM algorithm. The proposed class allocation approach produces the optimal location of subpixels by defining the cost function and calculating the corresponding cost of spatial arrangement of sub-pixels in different visiting order of classes. The technique is applied to Worldview-3 and ROSIS-03 images. A comparison between the results obtained through the proposed approach and an existing super-resolution mapping technique is introduced. The results show that the proposed algorithm is able to produce higher SRM accuracy than the other approaches especially in linear feature and class boundaries. The improvement value of the adjusted Kappa coefficient of the proposed algorithm related to the spatial attraction model with the AUOC class allocation technique in the scale factor 2, is 0.053 and 0.032, respectively. |
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| Keywords: mixed pixel, soft-then-hard super resolution mapping, spatial-spectral attraction model, Class allocation |
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Full-Text [PDF 4038 kb]
(751 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2019/01/7 | Accepted: 2019/02/19 | ePublished ahead of print: 2022/06/8 | Published: 2022/06/8
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