计算机科学与探索
計算機科學與探索
계산궤과학여탐색
Journal of Frontiers of Computer Science & Technology
2015年
10期
1263-1270
,共8页
矩形算法%灰度共生矩阵(GLCM)%弹性网格%指纹%民居分类
矩形算法%灰度共生矩陣(GLCM)%彈性網格%指紋%民居分類
구형산법%회도공생구진(GLCM)%탄성망격%지문%민거분류
rectangle algorithm%gray level co-occurrence matrix (GLCM)%elastic grid%fingerprint%house classification
使用基于面积特征和形状特征的矩形算法可以从卫星图像中分离出民居目标，对于误识别疑似民居区域，需要进一步提取它们的纹理特征加以排除。并且需要设计旋转、放大和裁剪算子，对所有抽取目标进行大小和方向的标准化。弹性网格技术可以选取图像的多个特征行和特征列，而它们的相交把一个图像划分成多个特征子格。计算出每个子格的灰度共生矩阵（gray level co-occurrence matrix，GLCM）的几个经典特征值形成一个特征数组，可以反映子格的局部纹理特征。所有子格的特征数组顺序组合形成一个特征向量，可以反映这个图像的全局特征。基于改进弹性网格划分和子格GLCM特征值的指纹向量能够同时表征一个图像的局部纹理特征和全局统计特征。通过与不同年代的民居样本特征指纹的相似度比较，实现了古民居的精确识别与分类。实验表明，使用矩形算法抽取出民居目标的正确率为86.9%，使用基于弹性网格划分和GLCM特征值的民居指纹算法，古民居初步分类正确率超过97.4%。
使用基于麵積特徵和形狀特徵的矩形算法可以從衛星圖像中分離齣民居目標，對于誤識彆疑似民居區域，需要進一步提取它們的紋理特徵加以排除。併且需要設計鏇轉、放大和裁剪算子，對所有抽取目標進行大小和方嚮的標準化。彈性網格技術可以選取圖像的多箇特徵行和特徵列，而它們的相交把一箇圖像劃分成多箇特徵子格。計算齣每箇子格的灰度共生矩陣（gray level co-occurrence matrix，GLCM）的幾箇經典特徵值形成一箇特徵數組，可以反映子格的跼部紋理特徵。所有子格的特徵數組順序組閤形成一箇特徵嚮量，可以反映這箇圖像的全跼特徵。基于改進彈性網格劃分和子格GLCM特徵值的指紋嚮量能夠同時錶徵一箇圖像的跼部紋理特徵和全跼統計特徵。通過與不同年代的民居樣本特徵指紋的相似度比較，實現瞭古民居的精確識彆與分類。實驗錶明，使用矩形算法抽取齣民居目標的正確率為86.9%，使用基于彈性網格劃分和GLCM特徵值的民居指紋算法，古民居初步分類正確率超過97.4%。
사용기우면적특정화형상특정적구형산법가이종위성도상중분리출민거목표，대우오식별의사민거구역，수요진일보제취타문적문리특정가이배제。병차수요설계선전、방대화재전산자，대소유추취목표진행대소화방향적표준화。탄성망격기술가이선취도상적다개특정행화특정렬，이타문적상교파일개도상화분성다개특정자격。계산출매개자격적회도공생구진（gray level co-occurrence matrix，GLCM）적궤개경전특정치형성일개특정수조，가이반영자격적국부문리특정。소유자격적특정수조순서조합형성일개특정향량，가이반영저개도상적전국특정。기우개진탄성망격화분화자격GLCM특정치적지문향량능구동시표정일개도상적국부문리특정화전국통계특정。통과여불동년대적민거양본특정지문적상사도비교，실현료고민거적정학식별여분류。실험표명，사용구형산법추취출민거목표적정학솔위86.9%，사용기우탄성망격화분화GLCM특정치적민거지문산법，고민거초보분류정학솔초과97.4%。
House goals are separated from satellite image by using rectangle algorithm based on area feature and shape feature, and the mistakenly identified objects need to be eliminated from all separated objects based on their different texture features. Rotating, enlarging and cutting operations need to be designed to make those separated objects standardize with same size and direction. Elastic grid technique can choose several characteristic lines and columns of the image, and the intersections of them can partition an image into several characteristic grids. The classic relevant statistics of gray level co-occurrence matrix (GLCM) of each grid are computed to generate a characteristic fingerprint array, which comprehensively reflect the local texture features of the grid. The combination of finger-print arrays of all grids can generate the fingerprint vector of an image, which can reflect global features of the image. The feature vector generated from improved elastic grid partition and GLCM statistic can simultaneously characterize the local texture features and global statistic features of an image. Ancient houses are accurately identified and clas-sified by the similarity comparison to house samples of different periods based on their characteristic fingerprint vec-tors. Experiments show that the correct rate of separated house targets is about 86.9%by using rectangle algorithm, and using the house fingerprint algorithm based on elastic grid partition and GLCM statistics, the correct prelimi-nary classification rate of ancient houses is more than 97.4%.
