针对单帧复杂背景红外图像点目标检测算法存在复杂背景下处理效果不理想、处理时间长的问题,提出了一种层次卷积滤波检测算法。主要分为两个部分:第一,根据红外小目标特性,设计一种层次卷积滤波的算子,对图像进行滤波处理,实现图像中小目标的增效和背景抑制的效果;第二,采用基于最大值的自适应阈值方法,对图像进行二值化操作,过滤背景杂波,最终提取到待检测的目标。在大量不同背景红外图像中进行实验,论文算法在背景抑制因子和信噪比增益的性能量化结果上优于现有5种典型红外弱小目标检测算法的性能结果,且平均处理时间仅为高斯拉普拉斯(Laplacian of Gaussian,LoG)滤波算法的30.42%。通过实验对比,表明该层次卷积滤波算法可以有效解决在不同复杂背景下的红外图像中对小目标检测的问题。

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method. The source code of HSAN is shared at https://github.com/yueliu1999/HSAN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.

In the realm of infrared small-target detection, the weighted local contrast approaches, which seek to improve the targets by the defined weighted factors, have garnered a lot of interest. However, there are several problems with these methods as follows. 1) The vast number of local contrast sliding subwindows restricts the time efficiency. 2) The dim targets in the complicated backgrounds are incorrectly eliminated by the background suppression procedure. 3) The background noise in the complicated environment cannot be effectively muted. A simplified dual-weighted three-layer window local contrast method (SDWTLLCM) is suggested in this work as a solution to these issues. To extract tiny targets and suppress complicated backgrounds, a hierarchical convolution filtering window is first created. Then, even without subwindow division, a simple three-layer sliding window is created for time efficiency enhancement. The dual-weighted local contrast approach is also intended to minimize the background and further highlight tiny objects. Eventually, the tiny targets may be extracted more effectively using the adaptive threshold segmentation procedure. The vast experimental findings show that our suggested strategy is effective and efficient.

近年来,由于全球气候变化引起海平面上升导致一些海拔较低的国家陆地面积逐渐减少,甚至在未来面临完全丧失的风险.这些因环境不适宜生存而被迫迁移的难民越来越多.已有的工作大多侧重于研究环境难民(Environmental Displaced Persons,EDP)的权利保护以及如何安置等定性分析问题,本文则从一个全新的角度出发,定量分析与预测环境难民的数量随海平面上升的变化趋势,为针对其地安置与文化保护等政策制定提供一些前瞻性的数据分析.本文拟以马尔代夫首都-马累(Male)为研究对象,通过Google Earth Pro从现有卫星图上采集马累地区的海拔数据,在Matlab仿真环境中采用双三次插值法拟合出马累真实地表的三维曲面图并建立相应的地表光滑曲面方程.其次,建立灰色模型预测未来60年内海平面的上升趋势,在此基础上计算相应的马累陆地面积随海平面上升的减少量,最后结合马累的最大可容忍人口密度计算出在未来60年间马累环境难民的变化趋势.