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31.
Supervised classification of quad-polarimetric SAR images is often constrained by the availability of reliable training samples. Active learning (AL) provides a unique capability at selecting samples with high representation quality and low redundancy. The most important part of AL is the criterion for selecting the most informative candidates (pixels) by ranking. In this paper, class supports based on the posterior probability function are approximated by ensemble learning and majority voting. This approximation is statistically meaningful when a large enough classifier ensemble is exploited. In this work, we propose to use extreme learning machines and apply AL to quad-polarimetric SAR image classification. Extreme learning machines are ideal because of their fast operation, straightforward solution and strong generalization. As inputs to the so-called active extreme learning machines, both polarimetric and spatial features (morphological profiles) are considered. In order to validate the proposed method, results and performance are compared with random sampling and state-of-the-art AL methods, such as margin sampling, normalized entropy query-by-bagging and multiclass level uncertainty. Experimental results for four quad-polarimetric SAR images collected by RADARSAT-2, AirSAR and EMISAR indicate that the proposed method achieves promising results in different scenarios. Moreover, the proposed method is faster than existing techniques in both the learning and the classification phases. 相似文献
32.
随着极化合成孔径雷达系统的发展,Pol SAR数据在各个领域得到了广泛的应用。本文研究了Pol SAR数据在矿山监测领域的可行性。首先对Pol SAR数据进行滤波去噪等预处理;然后介绍了适合矿山地物分类的Cloude特征向量分解和Freeman分解方法,在极化分解的基础上采用了一种结合散射熵和Freeman分解的Wishart分类方法进行分类,最终得到矿山监测地物的分类图,并通过人工解译的方式对分类后的图像信息进行归类并建立数据库,得到矿山地区的地物分类图。以机载Pol SAR数据为例,得到了较好的实验结果。 相似文献
33.
Since its first flight in 2007, the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR (PolSAR) data in very high spatial resolution. It is possible to observe small spatial features in this type of data, offering the opportunity to explore structures in the images. In general, the structured scenes would present multimodal or spiky histograms. The finite mixture model has great advantages in modeling data with irregular histograms. In this paper, a type of important statistics called log-cumulants, which could be used to design parameter estimator or goodness-of-fit tests, are derived for the finite mixture model. They are compared with log-cumulants of the texture models. The results are adopted to UAVSAR data analysis to determine which model is better for different land types. 相似文献