AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

Novelli, Antonio; Aguilar, Manuel A.; Aguilar, Fernando J.; Nemmaoui, Abderrahim; Tarantino, Eufemia

doi:10.3390/rs9010040

Cited by 31 publications

(24 citation statements)

References 20 publications

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“…The classification is based on-but not restricted to-standard methods from principal components analysis (PCA), k-means and Mahalonobis distances [23], but is tailored specifically to optimize the classification of smaller ships and icebergs. The success of the classification method lies in choosing the best classifiers and thresholds that correctly identify most segments.…”

Section: Satellite Images and Methods Of Analysismentioning

confidence: 99%

“…A very useful classification method is k-means clustering or k-nearest neighbor, where k objects are assigned a number of classifiers [23]. For example, a ship should have small B/L and area A smaller than ca.…”

Section: Spatial Classification Of Segments As Objectsmentioning

confidence: 99%

“…[21][22][23][24] for other segmentation methods. The number of segments vs. area, which is the same as the number of objects vs. area, is shown in Figure 3 for the satellite images of Figures 1 and 8b for two different thresholds.…”

Section: Spatial Classification Of Segments As Objectsmentioning

confidence: 99%

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Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Heiselberg¹,

Heiselberg

2017

Remote Sensing

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Abstract:The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats.

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Section: Satellite Images and Methods Of Analysismentioning

confidence: 99%

Section: Spatial Classification Of Segments As Objectsmentioning

confidence: 99%

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Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Heiselberg¹,

Heiselberg

2017

Remote Sensing

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“…The calculation of ED2 is a good example. It uses the AssesSeg tool, a command line tool to quantify image segmentation quality [51], which allows easy and fast segmentation evaluation.…”

Section: Discussionmentioning

confidence: 99%

Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy

et al. 2018

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High spatial resolution (HSR) image segmentation is considered to be a major challenge for object-oriented remote sensing applications that have been extensively studied in the past. In this paper, we propose a fast and efficient framework for multiscale and multifeatured hierarchical image segmentation (MMHS). First, the HSR image pixels were clustered into a small number of superpixels using a simple linear iterative clustering algorithm (SLIC) on modern graphic processing units (GPUs), and then a region adjacency graph (RAG) and nearest neighbors graph (NNG) were constructed based on adjacent superpixels. At the same time, the RAG and NNG successfully integrated spectral information, texture information, and structural information from a small number of superpixels to enhance its expressiveness. Finally, a multiscale hierarchical grouping algorithm was implemented to merge these superpixels using local-mutual best region merging (LMM). We compared the experiments with three state-of-the-art segmentation algorithms, i.e., the watershed transform segmentation (WTS) method, the mean shift (MS) method, the multiresolution segmentation (MRS) method integrated in commercial software, eCognition9, on New York HSR image datasets, and the ISPRS Potsdam dataset. Computationally, our algorithm was dozens of times faster than the others, and it also had the best segmentation effect through visual assessment. The supervised and unsupervised evaluation results further proved the superiority of the MMHS algorithm.

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“…Indeed, there are many works in the remote sensing field for segmentation quality assessment, including tools for tuning segmentation parameter values [35,36]. Proposed frameworks in the field of medical images enable only visual quality inspection of the segmentation results [37].…”

Section: Introductionmentioning

confidence: 99%

MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques

2017

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Abstract:In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate on specific regions of the brain in order to analyze them. However, segmentation of brain images is a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Among various methods proposed in the literature, clustering algorithms prove to be successful tools for image segmentation. In this paper, we present a framework for image segmentation that is devoted to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images. Furthermore, it enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results. To show the potential of the framework, the implemented clustering methods are compared on simulated T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR database) provided with ground truth segmentation.

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AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

Cited by 31 publications

References 20 publications

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy

MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques

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