期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 29, 期 -, 页码 757-767出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2019.2933747
关键词
Change detection; heterogeneous sensors; iterative conditional estimation (ICE); Markov random field (MRF); multimodal remote sensing; multisource data; multisensors; parameter estimation; pixel pairwise modeling; stochastic optimization; unsupervised Markovian segmentation
资金
- Computer Research Institute of Montreal (CRIM)
- Ministry of Economic Science and Innovation (MESI) of the Government of Quebec
This work presents a Bayesian statistical approach to the multimodal change detection (CD) problem in remote sensing imagery. More precisely, we formulate the multimodal CD problem in the unsupervised Markovian framework. The main novelty of the proposed Markovian model lies in the use of an observation field built up from a pixel pairwise modeling and on the bitemporal heterogeneous satellite image pair. Such modeling allows us to rely instead on a robust visual cue, with the appealing property of being quasi-invariant to the imaging (multi-) modality. To use this observation cue as part of a stochastic likelihood model, we first rely on a preliminary iterative estimation technique that takes into account the variety of the laws in the distribution mixture and estimates the parameters of the Markovian mixture model. Once this estimation step is completed, the Maximum a posteriori (MAP) solution of the change detection map, based on the previously estimated parameters, is then computed with a stochastic optimization process. Experimental results and comparisons involving a mixture of different types of imaging modalities confirm the robustness of the proposed approach.
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