光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2014年
2期
526-531
,共6页
李娜%李咏洁%赵慧洁%曹扬
李娜%李詠潔%趙慧潔%曹颺
리나%리영길%조혜길%조양
高光谱遥感%分类%马尔可夫随机场%概率支持向量机%高效置信传播
高光譜遙感%分類%馬爾可伕隨機場%概率支持嚮量機%高效置信傳播
고광보요감%분류%마이가부수궤장%개솔지지향량궤%고효치신전파
Hyperspectral remote sensing%Classification%Markov random field%Probabilistic support vector machine%Efficient belief propagation
针对仅利用光谱信息进行分类未充分利用高光谱数据图谱合一特性的问题，提出了基于马尔可夫随机场的改进分类模型，利用基于最大后验概率的马尔科夫随机场模型进行光谱与空间信息的融合应用，采用基于光谱信息的概率支持向量机方法提高马尔科夫随机场模型中光谱能量函数项的类条件概率估计精度，设计基于信息传播策略、信息更新策略、多尺度传播策略的多重加速策略的高效置信传播优化算法，解决了马尔科夫随机场模型中全局能量最小化优化过程中计算复杂度高、计算耗时等问题。利用航空可见-近红外成像光谱仪AVIRIS对美国印第安纳州西北部的农业示范区数据进行应用分析，并与迭代条件模型、模拟退火、置信传播等方法进行性能比较，试验结果表明：该方法能够达到总体分类精度95.78％、Kappa系数0.9334，优于现有马尔科夫随机场分类算法，并且计算效率比置信传播优化算法提高了3倍以上。
針對僅利用光譜信息進行分類未充分利用高光譜數據圖譜閤一特性的問題，提齣瞭基于馬爾可伕隨機場的改進分類模型，利用基于最大後驗概率的馬爾科伕隨機場模型進行光譜與空間信息的融閤應用，採用基于光譜信息的概率支持嚮量機方法提高馬爾科伕隨機場模型中光譜能量函數項的類條件概率估計精度，設計基于信息傳播策略、信息更新策略、多呎度傳播策略的多重加速策略的高效置信傳播優化算法，解決瞭馬爾科伕隨機場模型中全跼能量最小化優化過程中計算複雜度高、計算耗時等問題。利用航空可見-近紅外成像光譜儀AVIRIS對美國印第安納州西北部的農業示範區數據進行應用分析，併與迭代條件模型、模擬退火、置信傳播等方法進行性能比較，試驗結果錶明：該方法能夠達到總體分類精度95.78％、Kappa繫數0.9334，優于現有馬爾科伕隨機場分類算法，併且計算效率比置信傳播優化算法提高瞭3倍以上。
침대부이용광보신식진행분류미충분이용고광보수거도보합일특성적문제，제출료기우마이가부수궤장적개진분류모형，이용기우최대후험개솔적마이과부수궤장모형진행광보여공간신식적융합응용，채용기우광보신식적개솔지지향량궤방법제고마이과부수궤장모형중광보능량함수항적류조건개솔고계정도，설계기우신식전파책략、신식경신책략、다척도전파책략적다중가속책략적고효치신전파우화산법，해결료마이과부수궤장모형중전국능량최소화우화과정중계산복잡도고、계산모시등문제。이용항공가견-근홍외성상광보의AVIRIS대미국인제안납주서북부적농업시범구수거진행응용분석，병여질대조건모형、모의퇴화、치신전파등방법진행성능비교，시험결과표명：해방법능구체도총체분류정도95.78％、Kappa계수0.9334，우우현유마이과부수궤장분류산법，병차계산효솔비치신전파우화산법제고료3배이상。
The spatial correlativity and spectral information are not applied synchronously in the classification model of hyper-spectral data .To solve this problem ,an improved classification approach based on Markov random field (MRF) theory is pro-posed in our work .The MRF model based on maximum a posteriori is applied to combine the spectral and spatial information . The probabilistic support vector machine (PSVM ) algorithm using pixels'spectral information is applied to improve the estima-tion accuracy of the class conditional probability (CCP) of the spectral energy function ,and the efficient belief propagation (EBP) based on multi-accelerated strategy (such as ordinal propagated message strategy ,linearized message-updating strategy , and coarse-to-fine approach) is developed in order to solve the problem of the high calculational complexity and time-consumed in the global energy minimum optimization of MRF model .The true hyperspectral data collected by airborne visible infrared ima-ging spectrometer (AVIRIS) is applied to estimate the performance of the proposed approach in the agricultural demonstration area ,Indiana northwest ,USA .The performance of the proposed approach is compared with simulated annealing and iterated conditional model .The results illuminate that the average classification accuracy of our method reachs to 95.78% ,and the Kappa coefficient is 93.34% ,much higher than that of the result by the traditional MRF classification algorithms ,and the com-putational efficiency is improved more than 3 times compared with the belief propagation algorithm .
