發(fā)布時(shí)間：2018-06-29 12:05

  本文選題：遙感網(wǎng)絡(luò) + 遙感圖像　； 參考：《控制工程》2017年05期

【摘要】：遙感網(wǎng)絡(luò)所傳輸?shù)男∧繕?biāo)圖像在遙感圖像環(huán)境中容易與背景圖像的中央周邊差產(chǎn)生視覺誤差,造成遙感圖像的視覺顯著性檢測效果不好�？紤]局部像素的特征與相鄰的像素特征之間的差異,對中央周邊差的局部顯著性進(jìn)行變鄰域搜索,提出一種面向遙感網(wǎng)絡(luò)的遙感圖像小目標(biāo)圖像顯著性亮點(diǎn)檢測算法,進(jìn)行隨機(jī)場鄰域系統(tǒng)設(shè)計(jì),構(gòu)建小目標(biāo)圖像顯著亮點(diǎn)NSDFB頻域分解結(jié)構(gòu),基于多尺度的DoG濾波器疊加,求解目標(biāo)特征在圖像中的稀有度且評定其顯著性,采用變鄰域搜索方法實(shí)現(xiàn)對顯著亮點(diǎn)檢測模型的改進(jìn)。實(shí)驗(yàn)結(jié)果表明,該算法進(jìn)行遙感應(yīng)用場景的小目標(biāo)顯著亮點(diǎn)檢測分類結(jié)果性能較好,在遙感通信領(lǐng)域的小目標(biāo)識別和多媒體信息特征檢測檢索等領(lǐng)域應(yīng)用價(jià)值高。
[Abstract]:In the remote sensing image environment, the small target image transmitted by the remote sensing network can easily produce visual error with the central and peripheral difference of the background image, resulting in poor visual significance detection effect of the remote sensing image. Considering the difference between the local pixel feature and the adjacent pixel feature, the local saliency of central and peripheral difference is searched by variable neighborhood search, and a new algorithm for detecting saliency of small target image in remote sensing image is proposed, which is oriented to remote sensing network. The random field neighborhood system is designed to construct the NSDFB frequency domain decomposition structure of the small target image. Based on the DoG filter superposition of multi-scale, the rarity of the target feature in the image is solved and its significance is evaluated. The variable neighborhood search method is used to improve the model of bright spot detection. The experimental results show that the algorithm has good performance in detecting and classifying small target bright spots in remote sensing application scene, and has high application value in the field of remote sensing communication field, such as small target recognition and multimedia information feature detection and retrieval.
【作者單位】： 欽州學(xué)院資環(huán)學(xué)院;欽州學(xué)院人文學(xué)院;廣西科技大學(xué)軟件學(xué)院;
【基金】：廣西高校社科項(xiàng)目(SK13LX443)
【分類號】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 朱中洋;肖志云;孫光民;齊詠生;;基于Radon小波低分辨率的織物疵點(diǎn)檢測算法[J];計(jì)算機(jī)應(yīng)用;2015年03期

2 賀良杰;馬宏;高智勇;;基于局部對比和全局稀有度的顯著性檢測[J];計(jì)算機(jī)應(yīng)用研究;2014年09期

3 魏龍生;羅大鵬;;基于視覺注意機(jī)制的遙感圖像顯著性目標(biāo)檢測[J];計(jì)算機(jī)工程與應(yīng)用;2014年19期

4 侯毅;周石琳;雷琳;趙鍵;;基于Gabor濾波器組的多特征尺度不變特征提取方法[J];電子學(xué)報(bào);2013年06期

5 李釗寶;周銘;顧莉;;基于小波變換的相似多目標(biāo)識別算法研究[J];科技通報(bào);2013年03期

6 陸凱;李成金;趙勛杰;鄒薇;張雪松;;一種快速的亞像素圖像配準(zhǔn)算法[J];紅外技術(shù);2013年01期

7 郭哲;張艷寧;林增剛;;多信息融合的多姿態(tài)三維人臉面部五官標(biāo)志點(diǎn)定位方法[J];計(jì)算機(jī)學(xué)報(bào);2012年01期

8 楊h，

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