In remote sensing of the earth, polarimetric synthetic aperture radar (PolSAR) is an important imaging system because it is free from the weather and the time of night and day. In contrast to optical image represented by intensity or color, the PolSAR image is represented by measuring the complex scattering matrix as polarimetric properties including the phase and the amplitude information from SAR image acquisition step. This properties is often an obstacle to apply texton approach to PolSAR image directly.
Texton approach is known to be a well-established technique in texture research such as texture classification and segmentation. In this work, we proposed patch-based textons for feature extraction from PolSAR images by clustering local image neighborhoods including the PolSAR image properties directly. In clustering step, Wishart-distance measure was used to compute distance between two complex matrices including polarimetric properties instead of Euclidean distance in K-means clustering algorithm. Texton dictionary was used as a texture descriptor for texture classification.
The performance of patch-based textons in PolSAR images was evaluated with texture classification in the way of machine learning and 5-fold cross validation. The experimental data is divided into five predefined categories such as city, field, forest, grassland, and street. The experimental results depending on the way of choosing sample patches, speckle reduction, feature dimension for textons, the number of textons, patch size, and the parameter K for K-nearest neighbor were evaluated by the balanced accuracy. The balanced accuracy was used to evaluate the classification result instead of overall accuracy to adjust the influence of imbalanced samples. The proposed textons was compared with grayscale textons. Finally, we showed suitability of patch-based textons for PolSAR images.
PolSAR 영상 시스템은 날씨와 광량에 상관없이 이미지를 얻을 수 있기 때문에 remote sensing 분야에서 활용되고 있다. 컬러 혹은 intensity 정보로 표현되는 일반적인 광학 이미지와 달리, PolSAR 영상은 위상과 진폭정보를 갖는 복소수 행렬의 형태로 표현된다. 이러한 특징은 텍스쳐 분할에 주로 사용되는 텍스톤 기법을 PolSAR 영상에 적용하는 것을 어렵게 한다.
텍스톤 기법은 텍스쳐 연구분야에서 잘 정립되어 사용되어 온 기법이다. 본 연구에서는 PolSAR 영상 특징 추출을 위한 패치 기반 텍스톤 기법을 제안하였다. 복소수 행렬 연산을 위한 Wishart 거리 측정을 기반으로 수정된 K-means 기법을 도입하였다. 머신러닝에 기반하여 텍스쳐 분할을 위한 프레임워크를 구축하고 각 단계에서 사용된 파라미터에 대한 실험을 수행하고 그 결과를 살펴보았다. 실험 결과를 통해 제시된 텍스톤 기법의 PolSAR 영상에 대한 적합성을 보였다.
