CAJ | 학술논문

为减少因大量的光谱信息带来的计算复杂及数据冗余带来的高光谱数据分类性能降低,该文提出一种非负稀疏图降维算法.首先,构建超完备块字典对高维高光谱数据进行非负稀疏表示.然后,根据块非负稀疏表示,分别构建内部非负稀疏图和惩罚非负稀疏图,基于单调递减函数定义边的权重以体现样本间的相似程度.最后,通过同时最大化异类和最小化同类非负稀疏重构样本间的距离,得到从高维到低维的最优映射关系,从而实现对高维高光谱数据的降维.AVIRIS 92AV3C高光谱数据上的实验结果表明,所提算法能以较少的训练样本获得较高的整体分类精度和Kappa系数.
위감소인대량적광보신식대래적계산복잡급수거용여대래적고광보수거분류성능강저,해문제출일충비부희소도강유산법.수선,구건초완비괴자전대고유고광보수거진행비부희소표시.연후,근거괴비부희소표시,분별구건내부비부희소도화징벌비부희소도,기우단조체감함수정의변적권중이체현양본간적상사정도.최후,통과동시최대화이류화최소화동류비부희소중구양본간적거리,득도종고유도저유적최우영사관계,종이실현대고유고광보수거적강유.AVIRIS 92AV3C고광보수거상적실험결과표명,소제산법능이교소적훈련양본획득교고적정체분류정도화Kappa계수.
@@@@In order to reduce the computation complexity resulted from large number of spectral information and to reduce the decline of classification performance resulted from data redundancy, a dimensionality reduction algorithm called non-negative sparse graph is proposed. At first, an over-complete block dictionary is constructed to realize the non-negative sparse representation of high-dimensional hyperspectral data. Then, according to the non-negative sparse representation, an inner non-negative sparsity graph and a penalty non-negative sparsity graph are built where the weights of edges are defined by a monotone decreasing function to embody the similarity degree of samples. At last, an optimal mapping from the high-dimensional space to a low-dimensional subspace can be obtained by simultaneously maximizing the distance between non-negative sparsity reconstruction samples of different classes and minimizing the distance between non-negative sparsity reconstruction samples of the same class, which makes the dimensionality reduction of high-dimensional hyperspectral data realized. Experimental results on AVIRIS 92AV3C hyperspectral data show that the proposed algorithm can obtain higher overall accuracy and Kappa coefficient with few training samples.