顾及地物空间关系的卫星影像水系信息提取

为了更好地提取影像阴影部分的信息,本文采用一种顾及地物间空间关系的水系提取方法,将地物之间的空间关系与面向对象的方法相结合。通过多尺度分割算法生成同质的影像对象,利用决策树、隶属度函数和阴影与建筑物的邻接关系对水体进行提取。以昆明某区域影像为实验数据进行了试验,试验结果表明,所提出的方法可以有效解决阴影对水系提取造成的影响,大大提高了水系提取的精度。     

遥感图像分割中的小波域改进信息割算法

针对传统信息割(IC)算法对存在灰度变化的图像易引起误分割的问题,提出了一种小波域改进信息割(W\\|MIC)算法。使用一个新的结合像素点灰度关联和空间关联的Parzen窗对IC进行改进,以降低灰度变化对分割的影响;将MIC引入到小波域,利用小波的平滑作用降低参数选取复杂度,改善图像分割效果。遥感图像分割实验结果表明,W\\|MIC可有效降低参数选择效应,不仅避免了灰度变化引起的误分割,而且还较好地保持了图像边缘。     

基于VirtuoZo的航测内业DOM生成技术研究

笔者基于长期从事正射影像图制作的工作经验,以VirtuoZo全数字摄影测量系统制作正射影像图为研究对象,探讨了正射影像图的制作流程,文中关于正射影像图的匹配与编辑等内容凝练了笔者长期的工作实践经验,对于从事相关工作的同行有着一定的参考价值和借鉴意义。     

基于高分辨率遥感影像数据的FNEA分割算法研究与应用分析

近年来,随着遥感技术和信息技术的发展,遥感影像空间分辨率也大大提高。基于像素的传统影像处理方式已经不能满足现实需求,面向对象的遥感影像分析已成趋势。因此,图像分割技术是进行其他图像处理与分析之前的关键步骤,分形网络演化多尺度分割算法（FNEA）是面向对象分析的核心。本文详细阐述了基于FNEA的多尺度分割算法的思想和流程,并结合遥感影像进行了分割实验与参数分析,对高分辨率遥感影像的研究与应用有一定指导意义。     

Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images

This paper presents a technique developed for the retrieval of the orientation of crop rows, over anthropic lands dedicated to agriculture in order to further improve estimate of crop production and soil erosion management. Five crop types are considered: wheat, barley, rapeseed, sunflower, corn and hemp. The study is part of the multi-sensor crop-monitoring experiment, conducted in 2010 throughout the agricultural season (MCM’10) over an area located in southwestern France, near Toulouse. The proposed methodology is based on the use of satellite images acquired by Formosat-2, at high spatial resolution in panchromatic and multispectral modes (with spatial resolution of 2 and 8 m, respectively). Orientations are derived and evaluated for each image and for each plot, using directional spatial filters (45° and 135°) and mathematical morphology algorithms. “Single-date” and “multi-temporal” approaches are considered. The single-date analyses confirm the good performances of the proposed method, but emphasize the limitation of the approach for estimating the crop row orientation over the whole landscape with only one date. The multi-date analyses allow (1) determining the most suitable agricultural period for the detection of the row orientations, and (2) extending the estimation to the entire footprint of the study area. For the winter crops (wheat, barley and rapeseed), best results are obtained with images acquired just after harvest, when surfaces are covered by stubbles or during the period of deep tillage (0.27 > R² > 0.99 and 7.15° > RMSE > 43.02°). For the summer crops (sunflower, corn and hemp), results are strongly crop and date dependents (0 > R² > 0.96, 10.22° > RMSE > 80°), with a well-marked impact of flowering, irrigation equipment and/or maximum crop development. Last, the extent of the method to the whole studied zone allows mapping 90% of the crop row orientations (more than 45,000 ha) with an error inferior to 40°, associated to a confidence index ranging from 1 to 5 for each agricultural plot.     

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.     

顾及有效区域的Voronoi图的正射影像拼接

无人机影像的重叠度通常较大(60%~80%),对纠正后的正射影像进行拼接处理时,往往会产生很大的数据冗余。同时,影像越靠近边缘处变形越大,影响拼接精度。提出了一种顾及有效区域的Voronoi图的拼接线自动选取方法,基于整体构建拼接线网络,并根据影像有效区域优化拼接线,保证拼接的精度和效率。实验表明,该方法切实可行。     

高分辨率遥感影像滤波算法综述

高分辨率卫星影像空间分辨率高,地物结构纹理信息突出,常用于土地利用监测、自然灾害预报等领域,但其所含的背景噪声影响了影像识别和分析的有效性和可靠性,因此,选取合适的滤波方法消除各种噪声成为遥感影像处理的首要任务。在遥感技术发展的几十年中,研究者们针对各种噪声类型已发展了多种滤波方法。本文分析了高分辨率遥感影像噪声的特点,介绍了一些传统的滤波算法和近年来广泛应用的新型滤波方法,并深入探讨各种滤波器的性能及其优缺点,为今后选择合适的算法消除高分辨率遥感影像噪声提供参考,最后对遥感影像滤波方法的发展前景进行了展望。     

上海市无居民海岛普查方法与资源分析

通过利用高分辨率遥感影像辅助确定海岛岸线,补充说明了海岛的界定方法,介绍了开展上海市无居民海岛地名普查的工作流程,并分析了上海市无居民海岛的分布及开发利用现状。     

地理国情普查影像解译自动化方案研究

随着空天地一体化对地观测传感网的逐步完善,积累了大量的空间遥感影像。地理国情普查项目进一步地推动了遥感技术的发展和遥感影像的使用,使遥感影像更好地服务于经济社会的发展和生态文明的建设。传统的依靠人工的地物信息提取方式不能满足需求,本文尝试影像解译自动化方案替代完全依靠人工采集的方式,提高遥感信息提取的效率。