將稀疏表示與協(xié)同表示結(jié)合的高光譜檢測(cè)

1、將稀疏表示與協(xié)同表示結(jié)合的高光譜檢測(cè)1.介紹高光譜圖像包含數(shù)百個(gè)連續(xù)的狹窄的光譜，這使相比傳統(tǒng)方法得到的寬帶多譜線的圖像而言， 它能更精確的識(shí)別不同材料，且使?jié)撛诘拿裼煤蛙娪脩?yīng)用成為可能。在過去的二十年里，在遙感目標(biāo)檢測(cè)方面的團(tuán)體在尋找識(shí)別人造目標(biāo)或低可能性材料，它有 被背景污染的光譜特征。由于高光譜圖像的高的光譜分辨率提供的強(qiáng)大的識(shí)別，所以它能提 供檢測(cè)這些目標(biāo)的優(yōu)點(diǎn)。Over the last two decades, target detection in remote sensing community seeks to discriminate man-made objects or

2、 low probability materials, which usually have spectral features contaminated by the background, and HSI offers advantage of detecting these objects due to powerful discrimination provided by its high spectral resolution.最近，對(duì)于高光譜圖像，一種基于稀疏表示的目標(biāo)檢測(cè)算法已經(jīng)被提出。稀疏表示發(fā)現(xiàn)了高光譜圖像中的橡素通?？梢杂靡恍﹣碜杂诨A(chǔ)或字典的元素來表示。 稀疏表示的系數(shù)可

3、以通過L1范數(shù)最小化求得，通過確定其系數(shù)，這個(gè)方法揭示其稀疏表示 的識(shí)別特點(diǎn)。在參考文獻(xiàn)12中提到，根據(jù)像素的領(lǐng)域部分通常表示為同一類這一事實(shí)，一 個(gè)聯(lián)合稀疏模型被用來思考在高光譜圖像中的空間相關(guān)性，進(jìn)而增加稀疏重建的準(zhǔn)確性。 在參考文獻(xiàn)15中提到，一個(gè)非局部版本協(xié)同表示法，在高光譜圖像分類中被介紹。在參考文獻(xiàn)16中提到，一個(gè)協(xié)同表示和非常規(guī)則的正則化空間17的聯(lián)合分類器被展示。 另外，我們當(dāng)前的工作是利用協(xié)同表示來處理異常情況檢測(cè)。在這里，協(xié)同表示意味著所有 的原子聯(lián)合表示一個(gè)單個(gè)像素點(diǎn)。并且每一個(gè)原子有公平的機(jī)會(huì)來參與表示除非一個(gè)正則 化被添加去調(diào)整它的重要性)，這不同于稀疏表示中的一對(duì)一

4、。Recently, a target detection algorithm based on sparse repre-sentation 11,12 has been proposed for HSI. The sparse represen-tation exploits the fact that pixels in HSI can usually be represented using only a few elements from a basis or dictionary 13,14. The sparse representation coefficients can b

5、e solved via an 1-norm minimization. The algorithm exploits the discriminative characteristics of sparse representation to determine its label. In 12, a joint sparsity model (JSM) was used to consider spatial correlation in HSI to increase the accuracy of sparse reconstruction due to the fact that n

6、eighboring pixels usually belong to the same class. In 15, a nonlocal version of collaborative representation, was introduced for HSI classification. In 16, a combination ofcollaborative representation and nearest regularized subspace (NRS) 17 classifier was presented. Besides, our recent work 18 em

7、ployed collaborative representation for anomaly detection. Here, collaborative representation means all the atoms “collaborate” on the representation of a single pixel, and each atom has the equal chance to participate in the representation (unless a regularization term is added to adjust its import

8、ance), which is different from the one-against-one style in sparse representation.稀疏表示與協(xié)同表示結(jié)合的高光這篇文章被如下組織：第二部分提供了一個(gè)譜檢測(cè)檢測(cè)器詳細(xì)的描述。第二部分討論了 pre-partitioning和post-partitioning表示法。第四部分呈現(xiàn)實(shí)驗(yàn)結(jié)果。最后在第五部分提出總結(jié)性結(jié)論。This paperisorganizedasfollows. Section 2 providesadetailed description oftheproposedCSCRdetector. Se

9、ction3 discusses pre-partitioning and post-partitioning for representation. Section 4 presents experimentalresults.Finally,concludingremarksare providedin Section 5.2.提出的目標(biāo)檢測(cè)器考慮到高光譜數(shù)據(jù)帶有標(biāo)本X XXJ，該標(biāo)本里的目標(biāo)樣本，其中d是光譜帶的條數(shù)，nt是目標(biāo)樣本的總數(shù)，并且背景字典；，nb是背景字典的樣本數(shù)量，令y為一個(gè)高光譜圖像，將稀疏表示與協(xié)同表示結(jié)合的主要目的是利用兩個(gè)類特殊字典， 去表示兩個(gè)近似y-稀疏表示圖像目標(biāo)樣本和y-協(xié)同表示圖像目標(biāo)樣本。協(xié)同表示的任務(wù)是找到一個(gè)權(quán)值向量ab，使得沃心-入球嶼最小的