基于最優(yōu)狀態(tài)的多波段全極化SAR數據ML分類方法
OPTIMAL STATE BASED ML CLASSIFICATION METHOD FOR MULTI-BAND AND FULL-POLARIZATION SAR DATA
-
摘要: 該文描述了一種對多波段、全極化AIRSAR圖像中的地物目標進行分類的最大似然(ML)分類算法。該算法的特點是利用極化SAR圖像的最優(yōu)狀態(tài)進行分類。本文描述了最優(yōu)狀態(tài)的搜索算法和地貌分類算法,并利用美國AIRSAR獲得的多波段(P,L和C)、全極化圖像數據對本算法進行檢驗。與利用單波段、單極化圖像數據得到的分類結果相比,本文提出的基于最優(yōu)狀態(tài)的分類算法可以顯著地提高分類精度。Abstract: An ML Classification algorithm that classifies the terrain object in the multi-band, full-polarization SAR image is described in this paper. Its main feature is that the optimal state of polarization SAR image is utilized to classify objects. The searching algorithm for the optimal state and the classification algorithm of terrain targets are provided, and the classifier s performance is verified using the multi-band (P, L and C band), full-polarization testing data that is acquired by AIRSAR. Compared with the single band, single polarization SAR data, the classification accuracy of the optimal state based classification algorithm is improved significantly.
-
Brown L M J.Conway J A,Macklin J T,Polarimetirc synthetic-aperture radar,fundamental concepts and analysis tools,Journal of Research,1991,9(1),23-25.[2]Howard A Z,Jakob J vand Zyl,Daniel N H,Imaging radar polarimetry from wave synthesis Journal of Geophysical Research,1987,92(B1),683-701.[3]Middelkoop H,Janssen L L F,Implementation of temporal relationships in knowledge based classification or satellite images Photogramm,Eng Remote Sensing,1991,57(7),937-945. -
計量
- 文章訪問數: 2310
- HTML全文瀏覽量: 124
- PDF下載量: 384
- 被引次數: 0
下載:
