光子学报
光子學報
광자학보
ACTA PHOTONICA SINICA
2010年
1期
178-183
,共6页
孙显%王宏琦%张道兵%胡岩峰%巩大亮
孫顯%王宏琦%張道兵%鬍巖峰%鞏大亮
손현%왕굉기%장도병%호암봉%공대량
图像处理%自动解译%特征融合%基于对象%城市遥感图像
圖像處理%自動解譯%特徵融閤%基于對象%城市遙感圖像
도상처리%자동해역%특정융합%기우대상%성시요감도상
Image process%Automatic interpretation%Features integration%Object based%Urban remote sensing image
为更全面有效地解译城市遥感图像,提出了一种新的基于多特征融合的自动解译方法.该方法定义对象网络来表达图像结构并获取更为准确的处理单元.在此基础上,综合分析颜色、纹理、形状和位置等众多特征,通过自适应的概率学习训练最优分类器并标记目标类别.方法中还结合上下文信息进行空间平滑,大大消除了噪音、遮挡等影响,矢量标绘后得到最终解译结果.实验表明,该方法准确率高、鲁棒性好,适用于多种遥感图像城市场景的自动解译.
為更全麵有效地解譯城市遙感圖像,提齣瞭一種新的基于多特徵融閤的自動解譯方法.該方法定義對象網絡來錶達圖像結構併穫取更為準確的處理單元.在此基礎上,綜閤分析顏色、紋理、形狀和位置等衆多特徵,通過自適應的概率學習訓練最優分類器併標記目標類彆.方法中還結閤上下文信息進行空間平滑,大大消除瞭譟音、遮擋等影響,矢量標繪後得到最終解譯結果.實驗錶明,該方法準確率高、魯棒性好,適用于多種遙感圖像城市場景的自動解譯.
위경전면유효지해역성시요감도상,제출료일충신적기우다특정융합적자동해역방법.해방법정의대상망락래표체도상결구병획취경위준학적처리단원.재차기출상,종합분석안색、문리、형상화위치등음다특정,통과자괄응적개솔학습훈련최우분류기병표기목표유별.방법중환결합상하문신식진행공간평활,대대소제료조음、차당등영향,시량표회후득도최종해역결과.실험표명,해방법준학솔고、로봉성호,괄용우다충요감도상성시장경적자동해역.
A new automatic approach based on multiple features integration is proposed to interpret urban remote sensing images more effectively and comprehensively.The approach builds a hierarchical objects network to organize image structure and gets precise processing units.Then the probabilistic learning integrating multiple features including colour,texture,shape and position are performed to train a best classifier,and label all of the objects according to their classification values.The approach also applies spatial smoothing which incorporates contextual information to eliminate the adverse effects caused by background disturbance,occlusion and so on.After vectorization procedure,final results are given.Experiments demonstrate that proposed approach achieve high exactness and robustness in interpreting manifold urban remote sensing images.