Modified detection scheme for locating phase jumps and reducing detection errors

Abstract

Most phase unwrapping algorithms shift the 2π phase jump pixels to obtain the unwrapped phases, while most filtering algorithms remove the noisy pixels to avoid the fault of unwrapped phases. Thus, finding the positions of phase jump pixels and noisy pixels is important. This study proposed a modified detection scheme developed from the originally published noise and phase jump detection scheme [Opt. Express 19, 3086 (2011)]. The original detection scheme finds the noise positions and phase jump positions, and then marks these pixels in two maps, namely, the noise map and the phase jump map. One 2π phase jump contains a 2π-high position and a 0-low position. However, the original detection scheme usually finds a 2π-high position and misses a corresponding 0-low position, or usually finds a 0-low position and misses a corresponding 2π-high position. Moreover, the original detection scheme produces detection errors, containing the repeated pixels of phase jump or the wrong pixels generated by noise. Fortunately, the proposed modified detection scheme can find both the 2π-high position and the corresponding 0-low position. Moreover, the detection errors are also reduced by the proposed modified detection scheme. The robustness of the modified detection scheme is demonstrated both numerically and experimentally.

Semi-Automated Analysis of Digital Photographs for Monitoring East Antarctic Vegetation

“…Standard red, green, blue (RGB) digital photography has been successfully utilized in vegetation studies to determine vegetation cover (Bennett et al, 2000;McCarthy and Zaniewski, 2001;Booth et al, 2005a;Laliberte et al, 2007b;Greenwood and Weisberg, 2009;Ko et al, 2009;Haywood and Stone, 2011;Kim et al, 2011), vegetation type (Ehlers et al, 2006;Lathrop et al, 2006;Luscier et al, 2006;Yu et al, 2006;Hájek, 2008;Greenwood and Weisberg, 2009;Laliberte et al, 2010;Michel et al, 2010;Cserhalmi et al, 2011;Whiteside et al, 2011) and vegetation changes over time (Bennett et al, 2000;Ehlers et al, 2006;Cserhalmi et al, 2011). As vegetation communities have complex characteristics, with patches varying in size, internal homogeneity and discreteness, it makes sense to analyze these communities based on combinations of their spatial and spectral patterns (Blaschke and Strobl, 2001). Object-based image analysis (OBIA) is a useful technique to analyze such communities, with images being segmented into relatively homogeneous areas to create meaningful objects for analysis (Blaschke and Strobl, 2001;Liu and Xia, 2010), with rules developed to isolate elements of interest.…”

“…As vegetation communities have complex characteristics, with patches varying in size, internal homogeneity and discreteness, it makes sense to analyze these communities based on combinations of their spatial and spectral patterns (Blaschke and Strobl, 2001). Object-based image analysis (OBIA) is a useful technique to analyze such communities, with images being segmented into relatively homogeneous areas to create meaningful objects for analysis (Blaschke and Strobl, 2001;Liu and Xia, 2010), with rules developed to isolate elements of interest. These rules are objective, and not prone to the errors associated with subjective human perception and interpretation of vegetation patterns (Elzinga et al, 1998;Van Coillie et al, 2014), making them ideal for use for long-term monitoring applications.…”

Opportunities and Constraints in Characterizing Landscape Distribution of an Invasive Grass from Very High Resolution Multi-Spectral Imagery

“…This variation poses a major problem for pixel-based landscape classifications, leading to losses of accuracy and the infamous “salt-and-pepper” speckle (Blaschke and Strobl, 2001; Blaschke, 2010). Previous studies have addressed this issue by using object-based image analysis (OBIA) where the images are first segmented into smaller regions (objects) via some of many available segmentation techniques, and these objects are subsequently classified into cover types (Baatz and Schäpe, 2000; Clinton et al, 2010).…”

“…In contrast, many successful analyses of invasive species have relied on narrowband hyperspectral data (Underwood et al, 2003; Pengra et al, 2007; Andrew and Ustin, 2008; Hestir et al, 2008; Santos et al, 2011), and hyperspectral capabilities have been generally superior to hyperspatial ones in such studies (Nagendra and Rocchini, 2008; Rocchini et al, 2015). However, the latter evidence was based on the shortcomings of pixel-based methods such as local spectral variability (Nagendra and Rocchini, 2008), which can be addressed by OBIA (Baatz and Schäpe, 2000; Blaschke and Strobl, 2001; Burnett and Blaschke, 2003). Furthermore, limitations associated with cost and availability of high-resolution hyperspectral platforms are still challenging for management with constrained budgets.…”