本文選題：目標邊緣提取 + 遙感圖像　； 參考：《圖學學報》2017年04期

【摘要】：為了從遙感圖像中提取出更為準確完整的目標邊緣,提出一種基于無下采樣Shearlet模極大值和改進數(shù)學形態(tài)學的目標邊緣提取方法。首先采用無下采樣Shearlet變換(NSST)將圖像分解成邊緣細節(jié)豐富的高頻分量和邊緣細節(jié)較少的低頻分量;然后結(jié)合不同分解程度下邊緣像素點處的系數(shù)關系,對高頻分量的各個子帶進行模極大值檢測,再經(jīng)過雙層掩膜篩選后得到高頻邊緣提取結(jié)果;對低頻分量采用改進的數(shù)學形態(tài)學方法,得到低頻邊緣提取結(jié)果;最后將上述兩部分融合,使用區(qū)域連通方法去除孤立點,得到最終的目標邊緣圖像。大量實驗結(jié)果表明,與Canny以及其他4種同類邊緣提取方法相比,本文方法所得邊緣定位準確、完整清晰、細節(jié)豐富,且抗噪能力強,為后續(xù)遙感圖像目標特征提取與識別奠定更好基礎。
[Abstract]:In order to extract more accurate and complete target edges from remote sensing images, a new method based on unsampled Shearlet modulus maximum and improved mathematical morphology is proposed. Firstly, the image is decomposed into high-frequency components with rich edge details and low-frequency components with less edge details by using non-down-sampling Shearlet transform. Every sub-band of the high frequency component is detected by the modulus maximum, then the high frequency edge is extracted by double mask screening, and the low frequency component is extracted by the improved mathematical morphology method. Finally, the two parts are fused and the final target edge image is obtained by using the region connectivity method to remove the outliers. A large number of experimental results show that compared with Canny and other four other similar edge detection methods, the proposed method is accurate, complete and clear, rich in details, and has strong anti-noise ability. It lays a better foundation for target feature extraction and recognition in the following remote sensing images.
【作者單位】： 南京航空航天大學電子信息工程學院;浙江大學CAD&CG國家重點實驗室;城市空間信息工程北京市重點實驗室;南京水利科學研究院港口航道泥沙工程交通行業(yè)重點實驗室;黃河水利委員會黃河水利科學研究院水利部黃河泥沙重點實驗室;哈爾濱工業(yè)大學城市水資源與水環(huán)境國家重點實驗室;
【基金】：國家自然科學基金項目(61573183) CAD&CG國家重點實驗室開放基金項目(A1519) 城市空間信息工程北京市重點實驗室開放基金項目(2014203) 港口航道泥沙工程交通行業(yè)重點實驗室開放基金項目 水利部黃河泥沙重點實驗室開放基金項目(2014006) 城市水資源與水環(huán)境國家重點實驗室開放基金項目(LYPK201304) 江蘇高校優(yōu)勢學科建設工程資助項目
【分類號】：TP751

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