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To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. To resolve this, a multi-level self-adaptive individual tree detection method was presented in this paper. The proposed method can be seen as a hybrid model, which combined the strength of both raster-based and point-based methods. Raster-based strategy was first used for achieving initial trees detection results, while the point-based strategy was adopted for optimizing the clustered trees. In the proposed method, crown width scales were estimated automatically. Meanwhile, multi-scales segmented results were fused together to take advantage of segmented results of both larger and small scales. Six different coniferous forests were adopted for testing. Experimental result shows that this study achieved the lowest omission and commission errors comparing with other three classical approaches. Meanwhile, the average F1 score in this paper is 0.84, which is much highest out of other methods.

Polarimetric synthetic aperture radar is an important technology for Earth observation. Generally, polarimetric calibration (PolCAL) should be performed before any quantitative applications. The reflection symmetric targets can relieve the dependence of PolCAL on calibrators. In this work, we use a helix-ratio feature to extract these targets. Through analysis, we find that the helix-ratio feature has a high tolerance to polarimetric distortions and is suitable for extracting samples in un-calibrated images for PolCAL. An adaptive segmentation algorithm is also developed to extract reflection symmetric targets automatically. Experiments with C- and X-band airborne images show that the extraction of reflection symmetric targets can improve the calibration accuracy of crosstalk. In addition, the proposed method can maintain the polarimetric signature of the targets in the calibration and can improve calibration performance when compared with other reflection symmetric target extraction methods.