Unsupervised saliency detection and a-contrario based segmentation for satellite images

Zhao, Junbo; Chen, Shuoshuo; Zhao, Diyang; Zhu, Hailun; Chen, Xiaoxiao

doi:10.1109/icsenst.2013.6727739

Cited by 5 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Relevant examples of satellite image processing show that segmentation applied to environmental mapping gives rise to a semantically meaningful detection of vegetation assemblages, which are equivalent to habitats [31]. Selected previous works on satellite image segmentation include various developed algorithms, e.g., discriminating the regions against neighbours by semantic approach and normalisation using deep features in network convergence [32], contrasting land categories using diversity in pixels and smoothing shapes of the regions [33], iterative mean-shift clustering optimisation [34], layering images and segmenting through the R-Convolutional Neural Networks (CNNs) [35], evaluating the saliency in pixels using weighted dissimilarities in patches [36], and extracting contours by simplification [37,38].…”

Section: Examples Of Tools and Softwarementioning

confidence: 99%

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Lemenkova

2023

Analytics

View full text Add to dashboard Cite

This paper presents the object detection algorithms GRASS GIS applied for Landsat 8-9 OLI/TIRS data. The study area includes the Sudd wetlands located in South Sudan. This study describes a programming method for the automated processing of satellite images for environmental analytics, applying the scripting algorithms of GRASS GIS. This study documents how the land cover changed and developed over time in South Sudan with varying climate and environmental settings, indicating the variations in landscape patterns. A set of modules was used to process satellite images by scripting language. It streamlines the geospatial processing tasks. The functionality of the modules of GRASS GIS to image processing is called within scripts as subprocesses which automate operations. The cutting-edge tools of GRASS GIS present a cost-effective solution to remote sensing data modelling and analysis. This is based on the discrimination of the spectral reflectance of pixels on the raster scenes. Scripting algorithms of remote sensing data processing based on the GRASS GIS syntax are run from the terminal, enabling to pass commands to the module. This ensures the automation and high speed of image processing. The algorithm challenge is that landscape patterns differ substantially, and there are nonlinear dynamics in land cover types due to environmental factors and climate effects. Time series analysis of several multispectral images demonstrated changes in land cover types over the study area of the Sudd, South Sudan affected by environmental degradation of landscapes. The map is generated for each Landsat image from 2015 to 2023 using 481 maximum-likelihood discriminant analysis approaches of classification. The methodology includes image segmentation by ‘i.segment’ module, image clustering and classification by ‘i.cluster’ and ‘i.maxlike’ modules, accuracy assessment by ‘r.kappa’ module, and computing NDVI and cartographic mapping implemented using GRASS GIS. The benefits of object detection techniques for image analysis are demonstrated with the reported effects of various threshold levels of segmentation. The segmentation was performed 371 times with 90% of the threshold and minsize = 5; the process was converged in 37 to 41 iterations. The following segments are defined for images: 4515 for 2015, 4813 for 2016, 4114 for 2017, 5090 for 2018, 6021 for 2019, 3187 for 2020, 2445 for 2022, and 5181 for 2023. The percent convergence is 98% for the processed images. Detecting variations in land cover patterns is possible using spaceborne datasets and advanced applications of scripting algorithms. The implications of cartographic approach for environmental landscape analysis are discussed. The algorithm for image processing is based on a set of GRASS GIS wrapper functions for automated image classification.

show abstract

Section: Examples Of Tools and Softwarementioning

confidence: 99%

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Lemenkova

2023

Analytics

View full text Add to dashboard Cite

show abstract

“…The main task of saliency detection is to detect regions or objects of interested in images. In recent years, saliency detection has been used widely in computer vision, including object recognition [3][4][5], image compression [6,7], image matching [8,9], and image segmentation [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Saliency Detection Method Based on Global Contrast

Yu¹,

Zhang²,

Wang

2015

IJSIP

View full text Add to dashboard Cite

To highlight the saliency object clearly from the foreground, we propose a saliency detection method based on global contrast with cluster. Due to the fact that background pixels usually have similar patches, we use cluster analysis to merge the background regions. By using mean shift filter, the background pixels with similar color level are clustered and the saliency calculation can be decreased a lot. In the method, we use the contrast of color feature with all the other pixels to compute the saliency map. A weight coefficient is utilized to improve the detection accuracy in global contrast differences evaluation. The results of extensive experiments on public dataset show that our method perform well and can highlight the salient object clearly against the other five state-of-the-art methods. Besides, we demonstrate that the applications in image segmentation and fusion with our saliency map can get satisfactory results.

show abstract

“…In order to achieve automatic, rapid, and accurate remote sensing target detection, saliency detection was introduced to the remote sensing field in the last decade. [1][2][3][4][5][6] This method imitates human visual attention to identify the attention-grabbing regions that may contain candidate objects.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-guided saliency detection for remote sensing images

Zhao

Wang

Shi

et al. 2015

J. Appl. Remote Sens

View full text Add to dashboard Cite

Abstract. Traditional saliency detection can effectively detect possible objects using an attentional mechanism instead of automatic object detection, and thus is widely used in natural scene detection. However, it may fail to extract salient objects accurately from remote sensing images, which have their own characteristics such as large data volumes, multiple resolutions, illumination variation, and complex texture structure. We propose a sparsity-guided saliency detection model for remote sensing images that uses a sparse representation to obtain the high-level global and background cues for saliency map integration. Specifically, it first uses pixel-level global cues and background prior information to construct two dictionaries that are used to characterize the global and background properties of remote sensing images. It then employs a sparse representation for the high-level cues. Finally, a Bayesian formula is applied to integrate the saliency maps generated by both types of high-level cues. Experimental results on remote sensing image datasets that include various objects under complex conditions demonstrate the effectiveness and feasibility of the proposed method.

show abstract

Unsupervised saliency detection and a-contrario based segmentation for satellite images

Cited by 5 publications

References 13 publications

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

A Saliency Detection Method Based on Global Contrast

Sparsity-guided saliency detection for remote sensing images

Contact Info

Product

Resources

About