An efficient image-mosaicing method based on multifeature matching

Zagrouba, Ezzeddine; Barhoumi, Walid; Amri, Slim

doi:10.1007/s00138-007-0114-y

Cited by 48 publications

(23 citation statements)

References 53 publications

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“…In addition, the analysis of the aerial flight images showed that the construction of mosaics from "parts" of an image, following the methodology described in this paper, facilitates the treatment of these parts without losing spectral information [61,62]. Thus, for the flight image from 2009, obtained as a mosaic, the maximum overall accuracy value (70.56%) was for the image with spatial resolution of 200 cm, applying the Maximum Likelihood method and having statistically significant differences (McNemar test Chi-square = 18.1, p < 0.05) from the Support Vector Machine method.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and Comparison of QuickBird and ADS40-SH52 Multispectral Imagery for Mapping Iberian Wild Pear Trees (Pyrus bourgaeana, Decne) in a Mediterranean Mixed Forest

Arenas‐Castro

Fernández-Haeger

Jordano-Barbudo

2014

Forests

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Abstract:The availability of images with very high spatial and spectral resolution from airborne sensors or those aboard satellites is opening new possibilities for the analysis of fine-scale vegetation, such as the identification and classification of individual tree species. To evaluate the potential of these images, a study was carried out to compare the spatial, spectral and temporal resolution between QuickBird and ADS40-SH52 imagery, in order to discriminate and identify, within the mixed Mediterranean forest, individuals of the Iberian wild pear (Pyrus bourgaeana). This is a typical species of the Mediterranean forest, but its biology and ecology are still poorly known. The images were subjected to different correction processes and data were homogenized. Vegetation classes and individual trees were identified on the images, which were classified from two types of supervised classification (Maximum Likelihood and Support Vector Machines) on a pixel-by-pixel basis. The classification values were satisfactory. The classifiers were compared, and Support Vector Machines was the algorithm that provided the best results in terms of overall accuracy. The QuickBird image showed higher overall accuracy (86.16%) when the Support Vector Machines algorithm was applied. In addition, individuals of Iberian wild pear were discriminated with probability of over 55%, when the Maximum Likelihood algorithm was applied. From the perspective of improving the sampling effort, these results OPEN ACCESSForests 2014, 5 1305 are a starting point for facilitating research on the abundance, distribution and spatial structure of P. bourgaeana at different scales, in order to quantify the conservation status of this species.

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Section: Discussionmentioning

confidence: 99%

Evaluation and Comparison of QuickBird and ADS40-SH52 Multispectral Imagery for Mapping Iberian Wild Pear Trees (Pyrus bourgaeana, Decne) in a Mediterranean Mixed Forest

Arenas‐Castro

Fernández-Haeger

Jordano-Barbudo

2014

Forests

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“…Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are commonly used. They are applied on the overlapping region O: ∑ ( ) ∑ ( ) A reconstruction error (E) measuring the mean of the absolute intensity differences between two successive images on the overlapping area (O) has also been used [18]. It is defined as…”

Section: Quality Assessmentmentioning

confidence: 99%

Cross Correlation versus Mutual Information for Image Mosaicing

Ghannam¹,

Lynn²

2013

IJACSA

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Abstract-This paper reviews the concept of image mosaicking and presents a comparison between two of the most common image mosaicing techniques. The first technique is based on normalized cross correlation (NCC) for registering overlapping 2D images of a 3D scene. The second is based on mutual information (MI). The experimental results demonstrate that the two techniques have a similar performance in most cases but there are some interesting differences. The choice of a distinctive template is critical when working with NCC. On the other hand, when using MI, the registration procedure was able to provide acceptable performance even without distinctive templates. But generally the performance when using MI with large rotation angles was not accurate as with NCC.

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“…Recently, the use of feature-based matching techniques has dominated the proposed mosaicing algorithms. Different image features have been successfully utilized in image mosaicing such as SIFT (Brown and Lowe, 2007;Jia et al, 2015;Liqian and Yuehui, 2010), Harris points (Zagrouba et al, 2009), and Speeded Up Robust Features (SURF) (Geng et al, 2012;Rong et al, 2009;Wang and Watada, 2015;Xingteng et al, 2015). According to the flight time, image acquisition frequency, and the required overlap, a typical UAV system acquires hundreds to thousands of images in one flight.…”

Section: Introductionmentioning

confidence: 99%

A Fast Approach for Stitching of Aerial Images

Moussa¹,

El-Sheimy²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

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An efficient image-mosaicing method based on multifeature matching

Cited by 48 publications

References 53 publications

Evaluation and Comparison of QuickBird and ADS40-SH52 Multispectral Imagery for Mapping Iberian Wild Pear Trees (Pyrus bourgaeana, Decne) in a Mediterranean Mixed Forest

Evaluation and Comparison of QuickBird and ADS40-SH52 Multispectral Imagery for Mapping Iberian Wild Pear Trees (Pyrus bourgaeana, Decne) in a Mediterranean Mixed Forest

Cross Correlation versus Mutual Information for Image Mosaicing

A Fast Approach for Stitching of Aerial Images

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