Characterizing spatial patterns of invasive species using sub-pixel classifications

Invasive species disrupt landscape patterns and compromise the functionality of ecosystem processes. Non-native saltcedar poses significant threats to native vegetation and groundwater resources in the southwestern U.S. and Mexico, and quantifying spatial and temporal distribution patterns is essential for monitoring its spread. Considerable research focuses on determining the accuracy of various remote sensing techniques for distinguishing saltcedar from native woody riparian vegetation through sub-pixel, or soft classifications. However, there is a lack of research quantifying spatial distribution patterns from these classifications, mainly because landscape metrics, which are commonly used to statistically assess these patterns, require bounded classes and cannot be applied directly to soft classifications. This study tests a new method for discretizing sub-pixel data to generate landscape metrics using a continuum of fractional cover thresholds. The developed approach transforms sub-pixel classifications into discrete maps compliant with metric terms and computes and interprets metric results in the context of the region to explain patterns in the extent, distribution, and connectivity of saltcedar in the Rio Grande basin. Results indicate that landscape metrics are sensitive to sub-pixel values and can vary greatly with fractional cover. Therefore spectral unmixing should be performed prior to metric calculations. Analysis of metric trends provides evidence that saltcedar has expanded away from the immediate riparian zones and is displacing native vegetation. This information, coupled with control management strategies, can be used to target remediation activities along the Rio Grande.     

Super-resolution land cover mapping with indicator geostatistics

Many satellite images have a coarser spatial resolution than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. Super-resolution or sub-pixel mapping aims at providing a fine resolution map of class labels, one that displays realistic spatial structure (without artifact discontinuities) and reproduces the coarse resolution fractions. In this paper, existing approaches for super-resolution mapping are placed within an inverse problem framework, and a geostatistical method is proposed for generating alternative synthetic land cover maps at the fine (target) spatial resolution; these super-resolution realizations are consistent with all the information available.More precisely, indicator coKriging is used to approximate the probability that a pixel at the fine spatial resolution belongs to a particular class, given the coarse resolution fractions and (if available) a sparse set of class labels at some informed fine pixels. Such Kriging-derived probabilities are used in sequential indicator simulation to generate synthetic maps of class labels at the fine resolution pixels. This non-iterative and fast simulation procedure yields alternative super-resolution land cover maps that reproduce: (i) the observed coarse fractions, (ii) the fine resolution class labels that might be available, and (iii) the prior structural information encapsulated in a set of indicator variogram models at the fine resolution. A case study is provided to illustrate the proposed methodology using Landsat TM data from SE China.     

基于亚像素块匹配和字典学习的超分辨率算法

针对基于字典学习算法的计算效率低,且大多局限于处理单帧图像的问题,提出了一种基于亚像素块匹配和字典学习的超分辨算法,以实现对多帧图像的重构。采用亚像素块匹配方法对图像进行配准,依据配准结果构造低分辨率字典,并通过计算辅助图像块与目标图像块的相似度来选择用于重构的图像块。在Matlab平台上,将该算法用于静态图像和视频图像处理,获得了较好的重构效果。     

两种基于空间相关性的遥感亚像元分类制图方法对比分析

为了对比基于Hopfield Neural Network(HNN)和几何绘图的遥感亚像元分类制图方法的具体性能,验证空间相关性原理用于遥感亚像元定位的可行性。以一个研究区的TM影像为对象,通过利用HNN和几何亚像元制图方法,获得该区的亚像元定位结果,对比分析了各方法的视觉效果、精度和时间复杂度。结果表明:空间相关性特性在两种方法中得到了良好的体现,为后续亚像元制图研究应用提供参考。     

Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.     

遥感图像分类方法评析与展望

从遥感图像分类的基本概念和特征出发,在对各算法进行划分基础上,分析评价了主要遥感图像分类方法的原理、特点,优势与限制,并阐明了各主要方法作用及其意义,指出目前存在的问题,展望了遥感图像分类方法的发展与应用前景。     

数字图像在零件尺寸测量中的应用

提出一种基于数字图像技术的零件尺寸的测量方法,从数字图像测量系统的建立、图像预处理、直线的Hough变换、圆的最小二乘法拟合、亚象素定位、系统标定等几个方面进行探讨,给出测量方法.从理论上和实践上证明本方法的可行性和正确性.     

一种亚像素级栅格文字粗细调整方法

为了保证栅格文字的印刷效果,通常需要在印前对栅格文字的笔画粗细进行调整。本文提出一种亚像素级调整方法,基于栅格文字的邻域窗口分析,做到范围和方向可控的调整。该方法将栅格文字图看作前景文字和背景图层叠加的结果,执行图文分离,计算文字部分各点的油墨浓度,通过邻域内笔画形态分析,调整其油墨浓度,调整后的文字效果可以类比矢量文字粗细调整的效果。该方法能适应于全局或任意方向(如纵向、横向、斜向等)的粗细调整,实验表明该方法效果良好,很有实用价值。     

Novel fuzzy test patterns and their application in the measurement of geometric characteristics of displays

One of the final steps in a display production line is the image alignment that includes the visual adjustment of the geometric parameters and the color of the image. Measurement of geometric characteristics using machine vision is a necessary function in the automatic alignment of displays’ image in the factory. A critical part in the measurement of the geometric attributes is to precisely locate a test pattern position on the display screen. In this paper we introduce novel patterns as fuzzy test patterns and present a novel algorithm to precisely locate the fuzzy test pattern in captured images of the display screen. We experimentally show that the application of the proposed fuzzy test pattern and its associated locating algorithm increases the precision and robustness of the geometric measurements of a display like a TV display. The use of this new measurement method in an auto-alignment system increases the adjustment accuracy, improves the reliability of the alignment system, and improves the quality of images on the display of the adjusted display sets.     

A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery

Accurate maps of rural linear land cover features, such as paths and hedgerows, would be useful to ecologists, conservation managers and land planning agencies. Such information might be used in a variety of applications (e.g., ecological, conservation and land management applications). Based on the phenomenon of spatial dependence, sub-pixel mapping techniques can be used to increase the spatial resolution of land cover maps produced from satellite sensor imagery and map such features with increased accuracy. Aerial photography with a spatial resolution of 0.25 m was acquired of the Christchurch area of Dorset, UK. The imagery was hard classified using a simple Mahalanobis distance classifier and the classification degraded to simulate land cover proportion images with spatial resolutions of 2.5 and 5 m. A simple pixel-swapping algorithm was then applied to each of the proportion images. Sub-pixels within pixels were swapped iteratively until the spatial correlation between neighbouring sub-pixels for the entire image was maximised. Visual inspection of the super-resolved output showed that prediction of the position and dimensions of hedgerows was comparable with the original imagery. The maps displayed an accuracy of 87%. To enhance the prediction of linear features within the super-resolved output, an anisotropic modelling component was added. The direction of the largest sums of proportions was calculated within a moving window at the pixel level. The orthogonal sum of proportions was used in estimating the anisotropy ratio. The direction and anisotropy ratio were then used to modify the pixel-swapping algorithm so as to increase the likelihood of creating linear features in the output map. The new linear pixel-swapping method led to an increase in the accuracy of mapping fine linear features of approximately 5% compared with the conventional pixel-swapping method.