基于改進(jìn)小波域隱馬爾可夫模型的遙感圖像分割
Remote-Sensing Image Segmentation Based on Improved Wavelet-Domain Hidden Markov Models
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摘要: 該文提出了一種基于改進(jìn)小波域隱馬爾可夫樹(HMT)模型進(jìn)行圖像分割的方法。該方法利用基于希爾伯特變換對(duì)的二維方向小波,這種小波變換具有平移不變性、方向檢測(cè)性好的特點(diǎn)。同時(shí)該方法還利用拓展HMT對(duì)該改進(jìn)小波域中尺度間的小波系數(shù)相關(guān)性進(jìn)行建模,并結(jié)合多背景融合技術(shù)進(jìn)行遙感圖像的分割,得到了優(yōu)于已有文獻(xiàn)的分割結(jié)果,而且與同類算法相比,降低了算法所需的計(jì)算量。
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關(guān)鍵詞:
- 隱馬爾可夫模型; 多尺度分割; 希爾伯特變換對(duì); 二維方向小波; 多背景
Abstract: Improved wavelet-domain HMT based remote-sensing image segmentation algorithm is proposed in this paper, The algorithm is based on 2-D directional wavelet, which is implemented via Hilbert transform pairs. The 2-D directional wavelet can provide both shift invariance and good directional selectivity. In this paper, the dependence of wavelet coefficients lied in inter scale is modeled efficiently, and a new segmentaion algorithm is produced by combining this with multicontext fusion method. A better segmentation result for remote-sensing image with smaller computational burden is obtained, -
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