CAJ | 학술논문

针对线性稀疏解混模型无法准确识别真实端元造成丰度估计误差较大的问题，本文提出一种基于自适应冗余字典的高光谱混合像元解混算法．该算法根据地物在空间上的连续性，以及高光谱数据中信号成分与光谱库中物质光谱的强相关性，首先保留每个像元在光谱库上投影系数大于设定阈值所对应的光谱，将其作为与每个像元信号成分最匹配的光谱集合；然后合并该集合以构建高光谱数据的自适应冗余字典；最后利用ADMM算法求解高光谱数据在该字典上的丰度矩阵．仿真和实际高光谱数据实验结果表明，本文所提出的算法可减小丰度估计误差，在信噪比为15～35 dB时，其丰度估计准确性高于性能较优的SUnSAL算法约1～2 dB．
침대선성희소해혼모형무법준학식별진실단원조성봉도고계오차교대적문제，본문제출일충기우자괄응용여자전적고광보혼합상원해혼산법．해산법근거지물재공간상적련속성，이급고광보수거중신호성분여광보고중물질광보적강상관성，수선보류매개상원재광보고상투영계수대우설정역치소대응적광보，장기작위여매개상원신호성분최필배적광보집합；연후합병해집합이구건고광보수거적자괄응용여자전；최후이용ADMM산법구해고광보수거재해자전상적봉도구진．방진화실제고광보수거실험결과표명，본문소제출적산법가감소봉도고계오차，재신조비위15～35 dB시，기봉도고계준학성고우성능교우적SUnSAL산법약1～2 dB．
In the linear sparse unmixing model of hyperspectral data,large estimation error of the fractional abundances of endmembers in each mixed pixel may be caused by the incorrect identification of endmembers. A novel sparse unmixing algorithm was proposed based on adaptive overcomplete dictionary. Firstly,according to the spatial continuity of ground objects and the strong correlation between signal components of the hyperspectral data and spectral signatures in the library,the signatures with the projection coefficients of each pixels larger than the preset threshold were grouped as an optimal subset of signatures that best match the signal component of each mixed pixel. Secondly, an adaptive overcomplete dictionary of hyperspectral data was constructed by combining such subsets. Finally,the fractional abundances in this dictionary were obtained using the alternating direction method of multipliers (ADMM ). Experimental results on synthetic and real hyperspectral data show that the proposed algorithm improves the accuracy of identifying endmembers,with the reduced abundance estimation error r. When the signal to noise ratio range from 15 to 35 dB,the accuracy of the abundance estimation is improved about 1 to 2 dB compared with SUnSAL (sparse unmixing by variable splitting and augmented Lagrangian).