Metric Tensor for Multicomponent Edge Detection

International audienceIn this paper, we present the use of differential geometry for the segmentation of multispectral images, which allows us to unify several known methods including projecting onto a particular axis or a particular plan. This is done by choosing a metric tensor on the feature space computing the pullback of the metric tensor and applying standard Di Zenzo algorithm

The Color Clifford Hardy Signal: Application to Color Edge Detection and Optical Flow

This paper introduces the idea of the color Clifford Hardy signal, which can be used to process color images. As a complex analytic function's high-dimensional analogue, the color Clifford Hardy signal inherits many desirable qualities of analyticity. A crucial tool for getting the color and structural data is the local feature representation of a color image in the color Clifford Hardy signal. By looking at the extended Cauchy-Riemann equations in the high-dimensional space, it is possible to see the connection between the different parts of the color Clifford Hardy signal. Based on the distinctive and important local amplitude and local phase generated by the color Clifford Hardy signal, we propose five methods to identify the edges of color images with relation to a certain color. To prove the superiority of the offered methodologies, numerous comparative studies employing image quality assessment criteria are used. Specifically by using the multi-scale structure of the color Clifford Hardy signal, the proposed approaches are resistant to a variety of noises. In addition, a color optical flow detection method with anti-noise ability is provided as an example of application.Comment: 13 page

Bridging structure and function in semi-arid ecosystems by integrating remote sensing and ground based measurements

The Southwestern US is projected to continue the current significant warming trend, with increased variability in the timing and magnitude of rainfall events. The effects of these changes in climate are already evident in the form of multi-year droughts which have resulted in the widespread mortality of woody vegetation across the region. Therefore, the need to monitor and model forest mortality and carbon dynamics at the landscape and regional scale is an essential component of regional and global climate mitigation strategies, and critical if we are to understand how the imminent state transitions taking place in forests globally will affect climate forcing and feedbacks. Remote sensing offers the only solution to multitemporal regional observation, yet many challenges exist with employing modern remote sensing solutions in highly stressed vegetation characteristic of semi-arid biomes, making one of the most expansive biomes on the globe also one of the most difficult to accu- rately monitor and model. The goal of this research was to investigate how changes in the structure of semi-arid woodlands following forest mortality impacts ecosystem function, and to determine how this question can be addressed using remotely sensed data sets. I focused primarily on Pinus edulis and Juniperous monosperma (piñon-juniper) woodlands, and took advantage of an existing manipulation experiment where mortality was imposed on all of the large piñon (¡ 7 cm dbh) in a 4 ha PJ woodland in 2009 and the ecosystem functional responses have been quantified using eddy covariance. A nearby intact PJ woodland, also instrumented with eddy covariance, was used as a control for this experiment. I tested the ability of high resolution remote sensing data to mechanistically describe the patterns in overstory mortality and understory green-up in this manipulated woodland by comparing it to the intact woodland, and observed the heterogeneous response of the understory as a function of cover type. I also investigated the relationship between changes in soil water content and the greenness of the canopy, noting that in the disturbed woodland, I observed a decoupling between how the canopy was measured remotely (e.g., via vegetation indices, VI) and photosynthesis. This is significant in that it potentially represents a significant source of error in using existing light use efficiency models of carbon uptake in these disturbed woodlands. This research also suggested that leveraging remote sensing data which measures in the red-edge portion of reflected light can provide increased sensitivity to the low leaf area, ephemeral pulses of greenup that were identified in the disturbed woodland, post-canopy mortality. Given these findings, I developed a hierarchy of simple linear models to test how well vegetation indices acquired through different spatial resolution sensors (Land- sat and RapidEye) were able to predict carbon uptake in both intact and disturbed piñon-juniper woodlands. The vegetation indices used were a moisture sensitive VI, and a red-edge leveraging VI from these sensors, and I compared estimates of carbon uptake derived from these models to the Gross Primary Productivity estimated from tower-based eddy covariance at both the manipulated and intact piñon-juniper sites. I determined that the red-edge VI and the moisture sensitive VI both constrained uncertainty associated with carbon uptake, but that the variability in satellite view angle from scene to scene can impose a significant amount of noise in sparse canopy ecosystems. Finally, given the extent and prevalence of J. monosperma across the region, and its complex growth morphology, I tested the ability of aerial lidar to quantify the biomass of juniper. In this simplified case study, I developed a method- ology to relate the volume of canopy measured via lidar to the equivalent stem area at the root crown. By working in a single species ecosystem, I circumvented many challenges associated with driving allometries remotely, but also present a work-flow that I intend to adapt to more complex systems, namely piñon-juniper woodlands. Together, this work describes and addresses existing challenges with respect to us- ing remote sensing to understand both the structure and function of piñon-juniper woodlands, and how it changes in response to widespread piñon mortality. It provides several new techniques to mitigate the difficulties associated with monitoring mortality / recovery dynamics, predicting canopy function, and determining ecosystem state parameters in these complex, sensitive biomes

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

With the development of remote sensing technology, the resolution of remote sensing images is improving, and the presentation of geomorphic information is becoming more and more abundant, the difficulty of identifying and extracting edge information is also increasing. This paper demonstrates an algorithm to detect the edges of remote sensing images based on Grünwald–Letnikov fractional difference and Otsu threshold. First, a convolution difference mask with two parameters in four directions is constructed by using the definition of the Grünwald–Letnikov fractional derivative. Then, the mask is convolved with the gray image of the remote sensing image, and the edge detection image is obtained by binarization with Otsu threshold. Finally, the influence of two parameters and threshold values on detection results is discussed. Compared with the results of other detectors on the NWPU VHR-10 dataset, it is found that the algorithm not only has good visual effect but also shows good performance in quantitative evaluation indicators (binary graph similarity and edge pixel ratio)