Multi-resolution segmentation of high-resolution remotely sensed imagery using marker-controlled watershed transform

Rizvi, Imdad; Mohan, B. Krishna; Bhatia, Peeyush

doi:10.1145/1980022.1980167

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…5 In another work, a pixel-based approach is used to segment the images of road and building areas; thereafter an object-based classification approach is incorporated to classify non-road and non-building like impervious surfaces. 6 Rizvi et al 7,8 proposed a framework to achieve multi-resolution segmentation of high resolution satellite imagery by incorporating first marker controlled watershed approach. Initially markers are selected from satellite data to identify objects in low and high resolution images.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Information Extraction From High Resolution Satellite Imagery

Singh

Garg

2013

Int. J. Image Grap.

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This paper presents a hybrid approach for extraction of information from high resolution satellite imagery and also demonstrates the accuracy achieved by the final extracted information. The hybrid technique comprises of improved marker-controlled watershed transforms and a nonlinear derivative method. It overcomes all the disadvantages of existing region-based and edge-based methods by incorporating aforesaid hybrid methods. It preserves the advantages of multi-resolution and multi-scale gradient approaches. Region-based segmentation also incorporates the watershed technique due to its better efficiency in segmentation. In principle, a proper segmentation can be performed perfectly by watershed technique on incorporating ridges. These ridges express as the object's boundaries according to the property of contour detection. On the other hand, the nonlinear derivative method is used for resolving the discrete edge detection problem. Since it automatically selects the best edge localization, which is very much useful for estimation of gradient selection. The main benefit of univocal edge localization is to provide a better direction estimation of the gradient, which helps in producing a confident edge reference map for synthetic images. The practical merit of this proposed method is to derive an impervious surface from emerging urban areas.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Information Extraction From High Resolution Satellite Imagery

Singh

Garg

2013

Int. J. Image Grap.

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“…To overcome these problems, a strategy was proposed by Meyer and Beucher (1990). The strategy is called marker-controlled segmentation [2].The goal of the marker controlled segmentation is to detect the presence of the homogenous regions from the image by a set of morphological operations. Markers are connected components belonging to an image [9] , [10].…”

Section: Related Workmentioning

confidence: 99%

“…Until now, a variety of techniques and algorithms have been proposed for image segmentation. The three main categories of image segmentation are: edge detection, clustering and region extraction [1], [2], [8]. Clustering consists of classifying a homogenous cluster and naming each cluster as different region.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of detecting sharp changes in image brightness is to capture important events and changes in the properties of the world. Finally, region extraction groups pixels into a set of regions based on similarity [2].Most segmentation techniques are based on region extraction [2], [3], [4]. Image enhancement is the method to enhance the low contrast image.…”

Section: Introductionmentioning

confidence: 99%

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Marker Controlled Watershed Segmentation Using Bit-Plane Slicing

Sivagami

Revathi

2013

IJIPVS

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Image segmentation is the basis for computer vision and object recognition. Watershed transform is one of the common methods used for region based segmentation. The previous watershed methods results in over segmentation. In this paper we present a novel method for efficient image segmentation by using bit-plane slicing and marker-controlled watershed. Bit-Plane slicing method produces the sliced image by taking the most significant bit of the image as the input to the bit-plane slicing algorithm. The output of the Bit-Plane slicing algorithm is used to produce the gradient image .The watershed segmentation algorithm is applied to the average of the marker image and the gradient image so as to get efficient segmentation result. Experimental results, shows that the proposed method reduces the memory consumption and computation.

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“…To date, the algorithm has been widely used in the field of remote sensing image information extraction [14][15][16]. In addition, dozens of improved watershed segmentation algorithms have been also proposed to solve the problems of image over-segmentation and obvious algorithm noise, such as an algorithm based on efficient computation of the shortest paths [17], a texture marker-controlled watershed segmentation algorithm [18], an edge embedded marker-based watershed segmentation algorithm [19], a wavelet transform in combination with marker-based watershed segmentation algorithm [20], and image segmentation including image enhancement and noise removal techniques with Prewitt's edge detection operator [21]. The improved methods include pre-improvement, post-improvement, or both; among these, the region merging based watershed segmentation algorithm [16,22,23] can be used after segmentation based on the characteristics of the region texture, color, and shape information.…”

Section: Introductionmentioning

confidence: 99%

Watershed Segmentation Algorithm Based on Luv Color Space Region Merging for Extracting Slope Hazard Boundaries

Zhang

Xue

et al. 2020

IJGI

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To accurately identify slope hazards based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm is proposed. The color difference of the Luv color space was used as the regional similarity measure for region merging. Furthermore, the area relative error for evaluating the image segmentation accuracy was improved and supplemented with the pixel quantity error to evaluate the segmentation accuracy. An unstable slope was identified to validate the algorithm on Chinese Gaofen-2 (GF-2) remote sensing imagery by a multiscale segmentation extraction experiment. The results show the following: (1) the optimal segmentation and merging scale parameters were, respectively, minimum threshold constant C for minimum area Amin of 500 and optimal threshold D for a color difference of 400. (2) The total processing time for segmentation and merging of unstable slopes was 39.702 s, much lower than the maximum likelihood classification method and a little more than the object-oriented classification method. The relative error of the slope hazard area was 4.92% and the pixel quantity error was 1.60%, which were superior to the two classification methods. (3) The evaluation criteria of segmentation accuracy were consistent with the results of visual interpretation and the confusion matrix, indicating that the criteria established in this study are reliable. By comparing the time efficiency, visual effect and classification accuracies, the proposed method has a good comprehensive extraction effect. It can provide a technical reference for promoting the rapid extraction of slope hazards based on remote sensing imagery. Meanwhile, it also provides a theoretical and practical experience reference for improving the watershed segmentation algorithm.

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Multi-resolution segmentation of high-resolution remotely sensed imagery using marker-controlled watershed transform

Cited by 8 publications

References 6 publications

A Hybrid Approach for Information Extraction From High Resolution Satellite Imagery

A Hybrid Approach for Information Extraction From High Resolution Satellite Imagery

Marker Controlled Watershed Segmentation Using Bit-Plane Slicing

Watershed Segmentation Algorithm Based on Luv Color Space Region Merging for Extracting Slope Hazard Boundaries

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