Unsupervised Change Detection using Thin Cloud-Contaminated Landsat Images
| Autor: | Huseyin Kusetogullari, Begum Karabatak, Engin Mendi, Amir Yavariabdi |
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| Rok vydání: | 2018 |
| Předmět: |
Markov random field
010504 meteorology & atmospheric sciences Pixel business.industry Computer science Stationary wavelet transform ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Pattern recognition 02 engineering and technology 01 natural sciences Image differencing Wavelet Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition Artificial intelligence Mean-shift business Change detection 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | IEEE Conf. on Intelligent Systems |
| DOI: | 10.1109/is.2018.8710473 |
| Popis: | In this paper, a novel unsupervised change detection method is proposed to automatically detect changes between two cloud-contaminated Landsat images. To achieve this, firstly, a photometric invariants technique with Stationary Wavelet Transform (SWT) are applied to input images to decrease the influence of cloud and noise artifacts in the change detection process. Then, mean shift image filtering is employed on the sub-band difference images, generated via image differencing technique, to smooth the images. Next, multiple binary change detection masks are obtained by partitioning the pixels in each of the smoothed sub-band difference images into two clusters using Fuzzy c-means (FCM). Finally, the binary masks are fused using Markov Random Field (MRF) to generate the final solution. Experiments on both semi-simulated and real data sets show the effectiveness and robustness of the proposed change detection method in noisy and cloud-contaminated Landsat images. |
| Databáze: | OpenAIRE |
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