Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 59, Issue 10, Pages 8568-8583Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3035642
Keywords
Edge preserving (EP); graph-cuts; hyperspectral image (HSI) classification; Markov random fields (MRFs)
Categories
Funding
- National Key Research and Development Program of China [2018AAA0102702]
- National Natural Science Foundation of China [62036007, 61772402, 61671339, 62002272, U1605252]
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This article introduces a novel approach for addressing the over-smoothing issue in MRF by class-by-class refinement and adaptive edge preservation. Experimental results demonstrate the superiority of aEPMs in evaluation metrics and detail preservation compared to traditional methods.
This article presents a novel adaptive edge preserving (aEP) scheme in Markov random fields (MRFs) for hyperspectral image (HSI) classification. MRF regularization usually suffered from over-smoothing at boundaries and insufficient refinement within class objects. This work divides and conquers this problem class-by-class, and integrates K (K - 1)/2 (K is the class number) aEP maps (aEPMs) in MRF model. Spatial label dependence measure (SLDM) is designed to estimate the interpixel label dependence for given spectral similarity measure. For each class pair, aEPM is optimized by maximizing the difference between intraclass and interclass SLDM. Then, aEPMs are integrated with multilevel logistic (MLL) model to regularize the raw pixelwise labeling obtained by spectral and spectral-spatial methods, respectively. The graph-cuts-based a fl-swap algorithm is modified to optimize the designed energy function. Moreover, to evaluate the final refined results at edges and small details thoroughly, segmentation evaluation metrics are introduced. Experiments conducted on real HSI data denote the superiority of aEPMs in evaluation metrics and region consistency, especially in detail preservation.
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