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	Assessing and Correcting Topographic Effects on Forest Canopy Height Retrieval Using Airborne LiDAR Data 期刊论文 OAI收割 SENSORS, 2015, 卷号: 15, 期号: 6, 页码: 939-948 作者: Duan, Zhugeng; Zhao, Dan; Zeng, Yuan; Zhao, Yujin; Wu, Bingfang 收藏 \| 浏览/下载：17/0 \| 提交时间：2016/04/20 LiDAR canopy height point cloud Dinghushan National Nature Reserve multi-resolution segmentation topography
	An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data 期刊论文 OAI收割 REMOTE SENSING, 2013, 卷号: 5, 期号: 10, 页码: 5346-5368 作者: Zhang, Wei; Li, Ainong; Jin, Huaan; Bian, Jinhu; Zhang, Zhengjian 收藏 \| 浏览/下载：45/0 \| 提交时间：2013/11/01 data fusion linear spectral mixing model multi-resolution segmentation sliding window temporal weight
	Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China Wu Z.-G.; Wang M.-J.; Han G.-L. 收藏 \| 浏览/下载：78/0 \| 提交时间：2013/03/25 Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.
	An Algorithm of Boundary Refinement Based on the Multi-Scale Feature for High Resolution Remote Sensing Images 会议论文 OAI收割 2009 International Conference on Wireless Communications and Signal Processing, New York Liu, Yuan; He, Guojin 收藏 \| 浏览/下载：18/0 \| 提交时间：2014/12/07 image segmentation multi-scale high resolution Gauss pyramid
	Research on tracking approach to low-flying weak small target near the sea (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Xue X.-C. 收藏 \| 浏览/下载：33/0 \| 提交时间：2013/03/25 Automatic target detection is very difficult in complicate background of sea and sky because of the clutter caused by waves and clouds nearby the sea-level line. In this paper in view of the low-flying target near the sea is always above the sea-level line we can first locate the sea-level line and neglect the image data beneath the sea-level line. Thus the noise under the sea-level line can be suppressed and the executive time of target segmentation is also much reduced. A new method is proposed which first uses neighborhood averaging method to suppress background and enhance targets so as to increase SNR and then uses the multi-point multi-layer vertical Sobel operator combined with linear least squares fitting to locate the sea-level line lastly uses the centroid tracking algorithm to detect and track the target. In the experiment high frame rate and high-resolution digital CCD camera and high performance DSP are applied. Experimental results show that this method can efficiently locate the sea-level line on various conditions of lower contrast and eliminate the negative impact of the clutter caused by waves and clouds and capture and track target real-timely and accurately.