Mixture-Based Superpixel Segmentation and Classification of SAR Images

Arisoy, Sertac; Kayabol, Koray

doi:10.1109/lgrs.2016.2605583

Cited by 51 publications

(21 citation statements)

References 15 publications

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“…Of particular interest here is the Mixture Model Superpixel algorithm (MISP) first proposed by Arisoy and Kayabol in [39]. MISP performs segmentation based on a finite mixture model (FMM) which takes a pixel's amplitude and spatial coordinates as features to be modelled.…”

Section: B Mixture Model Superpixelsmentioning

confidence: 99%

“…A great number of well-known distributions commonly encountered in SAR-related literature arise as special cases of the GΓD -these include the Gamma distribution (v = 1), the Weibull (κ = 1), the Rayleigh (v = 2, κ = 1), the exponential (v = 1, κ = 1), the Nakagami (v = 2) used by Arisoy and Kayabol in [39] and also the inverse Gamma (v = −1).…”

Section: The Generalised Gamma Distributionmentioning

confidence: 99%

“…In [39] the authors opt to model the amplitudes of pixels with the Nakagami distribution, which is a commonly employed multilook amplitude model for SAR images. Here however we opt for the GΓD (2) and hence a pixel's amplitude a n is modelled as…”

Section: Generalised Gamma Mixture Model Superpixelsmentioning

confidence: 99%

See 2 more Smart Citations

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

Pappas

Anantrasirichai

Achim

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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The extraction of river planforms from remotely sensed satellite images is a task of crucial importance to many applications such as land planning, water resource monitoring or flood prediction. In this paper we present a novel framework for the extraction of rivers from Synthetic Aperture Radar (SAR) images, based on superpixel segmentation and subsequent classification. Superpixel segmentation is achieved via a modelling of the image pixels' amplitudes and spatial coordinates as a finite mixture model, where the Generalised Gamma distribution is used to accurately model a variety of high-resolution SAR scenes. A number of features describing texture and statistics are extracted on a superpixel level, facilitating the identification of river superpixels-planforms are then extracted via unsupervised, agglomerative clustering thus eliminating the need for labelled training data. We present results of our proposed method on ICEYE-X2 and SENTINEL-1 SAR data demonstrating its ability to produce pixel-accurate river masks.

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Section: B Mixture Model Superpixelsmentioning

confidence: 99%

Section: The Generalised Gamma Distributionmentioning

confidence: 99%

See 1 more Smart Citation

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

Pappas

Anantrasirichai

Achim

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…It divides the adjacent pixel points with similar features into pixel blocks; the division is accomplished using specific rules according to certain algorithms. In order to obtain better superpixel segmentation results, this paper uses DBSCAN, SLIC, Turbopixel, and other superpixel segmentation algorithms to segment images [36]. These segmentation results are shown in Fig.…”

Section: A Superpixel Segmentationmentioning

confidence: 99%

Fast Two-Cycle Level Set Tracking With Interactive Superpixel Segmentation and Its Application in Image Retrieval

Pan

et al. 2019

IEEE Access

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In this paper, a fast two-cycle level set contour tracking algorithm based on interactive superpixel segmentation is proposed. Then, image retrieval is carried out based on the contour tracking results. The method of contour tracking is divided into three stages: superpixel segmentation, interactive feature extraction, and foreground tracking. In the first phase, the image is preprocessed by a superpixel segmentation method. In the second stage, the feature pools are defined and established interactively according to the complexity of the image. In the last stage, based on the first two stages, a fast two-cycle level set method is used to track the contour of the target object. Finally, the results of the contour tracking are applied to an image matting of the target foreground, and then the foreground image retrieval is implemented. The results of experimental comparisons demonstrate the effectiveness of the proposed method.

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“…In this regard, Bruzzone et al [23] proposed to use Gaussian distribution to model DI where an EM algorithm was used to find the threshold. The Markov random field (MRF) [24], the Fisher distribution [25], and the Nakagami distribution [26] were also used to model DI. Furthermore, the multinomial latent model [27] was applied on SAR images.…”

Section: Introductionmentioning

confidence: 99%

A self-paced learning algorithm for change detection in synthetic aperture radar images

et al. 2018

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Detecting changed regions between two given synthetic aperture radar images is very important to monitor change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach and has been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness.

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Mixture-Based Superpixel Segmentation and Classification of SAR Images

Cited by 51 publications

References 15 publications

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

Fast Two-Cycle Level Set Tracking With Interactive Superpixel Segmentation and Its Application in Image Retrieval

A self-paced learning algorithm for change detection in synthetic aperture radar images

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