Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis

Multiscale methods have become progressively valuable in geomorphometric analysis as data have become increasingly detailed. This paper evaluates the theoretical and empirical properties of several common scaling approaches in geomorphometry. Direct interpolation (DI), cubic convolution resampling (RES), mean aggregation (MA), local quadratic regression (LQR), and an efficiency optimized Gaussian scale-space implementation (fGSS) method were tested. The results showed that when manipulating resolution, the choice of interpolator had a negligible impact relative to the effects of manipulating scale. The LQR method was not ideal for rigorous multiscale analyses due to the inherently non-linear processing time of the algorithm and an increasingly poor fit with the surface. The fGSS method combined several desirable properties and was identified as an optimal scaling method for geomorphometric analysis. The results support the efficacy of Gaussian scale-space as a general scaling framework for geomorphometric analyses.

Spectrum decomposition in Gaussian scale space for uneven illumination image binarization

Although most images in industrial applications have fewer targets and simple image backgrounds, binarization is still a challenging task, and the corresponding results are usually unsatisfactory because of uneven illumination interference. In order to efficiently threshold images with nonuniform illumination, this paper proposes an efficient global binarization algorithm that estimates the inhomogeneous background surface of the original image constructed from the first k leading principal components in the Gaussian scale space (GSS). Then, we use the difference operator to extract the distinct foreground of the original image in which the interference of uneven illumination is effectively eliminated. Finally, the image can be effortlessly binarized by an existing global thresholding algorithm such as the Otsu method. In order to qualitatively and quantitatively verify the segmentation performance of the presented scheme, experiments were performed on a dataset collected from a nonuniform illumination environment. Compared with classical binarization methods, in some metrics, the experimental results demonstrate the effectiveness of the introduced algorithm in providing promising binarization outcomes and low computational costs.

Automatic Detection of Individual Trees from VHR Satellite Images Using Scale-Space Methods

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.

Efficient Complex High-Precision Computations on GPUs without Precision Loss

General-purpose computing on graphics processing units is the utilization of a graphics processing unit (GPU) to perform computation in applications traditionally handled by the central processing unit. Many attempts have been made to implement well-known algorithms on embedded and mobile GPUs. Unfortunately, these applications are computationally complex and often require high precision arithmetic, whereas embedded and mobile GPUs are designed specifically for graphics, and thus are very restrictive in terms of input/output, precision, programming style and primitives available. This paper studies how to implement efficient and accurate high-precision algorithms on embedded GPUs adopting the OpenGL ES language. We discuss the problems arising during the design phase, and we detail our implementation choices, focusing on the SIFT and ALP key-point detectors. We transform standard, i.e., single (or double) precision floating-point computations, to reduced-precision GPU arithmetic without precision loss. We develop a desktop framework to simulate Gaussian Scale Space transforms on all possible target embedded GPU platforms, and with all possible range and precision arithmetic. We illustrate how to re-engineer standard Gaussian Scale Space computations to mobile multi-core parallel GPUs using the OpenGL ES language. We present experiments on a large set of standard images, proving how efficiency and accuracy can be maintained on different target platforms. To sum up, we present a complete framework to minimize future programming effort, i.e., to easily check, on different embedded platforms, the accuracy and performance of complex algorithms requiring high-precision computations.