一種聯(lián)合陰影和目標區(qū)域圖像的SAR目標識別方法
doi: 10.11999/JEIT140713 cstr: 32379.14.JEIT140713
基金項目:
國家自然科學基金(61201292, 61322103, 61372132)，全國優(yōu)秀博士學位論文作者專項資金(FANEDD-201156)，國家部委基金和中央高?；究蒲袠I(yè)務費專項資金資助課題
SAR Target Recognition by Combining Images of the Shadow Region and Target Region
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摘要: 地面目標的SAR圖像中除了包含目標散射回波形成的區(qū)域，還包括由目標遮擋地面形成的陰影區(qū)域。但是由于這兩種區(qū)域中的圖像特性不相同，所以傳統(tǒng)的SAR圖像自動目標識別主要利用目標區(qū)域信息進行目標識別，或者單獨使用陰影區(qū)域進行識別。該文提出一種陰影區(qū)域與目標區(qū)域圖像聯(lián)合的稀疏表示模型。通過使用1\2范數(shù)最小化方法求解該模型得到聯(lián)合的稀疏表示，然后根據(jù)聯(lián)合重構誤差最小準則進行SAR圖像目標識別。在運動和靜止目標獲取與識別(MSTAR)數(shù)據(jù)集上的識別實驗結果表明，通過聯(lián)合稀疏表示模型可以有效地將目標區(qū)域與陰影區(qū)域信息進行融合，相對于采用單獨區(qū)域圖像的稀疏表示識別方法性能更好。
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關鍵詞:
- SAR /
- 目標識別 /
- 聯(lián)合稀疏表示 /
- 1\2范數(shù)最小化
Abstract: SAR image of the ground target contains the target region formed by the scattered echoes of the target as well as the shadow area. However, the characteristics of the two areas are essentially different, therefore the traditional SAR image Automatic Target Recognition (ATR) methods use mainly target area information alone or shadow region only for recognition. This paper presents a joint sparse representation model by combining images of the shadow region and target region. By using the1\2 norm minimization method to solve the joint sparse representation model, the SAR image target recognition is achieved by minimizing the joint reconstruction error. Recognition results on Moving and Stationary Target Acquisition and Recognition (MSTAR) data sets show that the joint sparse representation model can effectively fuse the information within the target region and shadow region, and it has much better recognition performance than the methods using only the target or shadow area information of the image.-
Key words:
- SAR /
- Target recognition /
- Joint sparse representation /
- 1\2-norm minimization
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