A REVIEW ON VARIOUS METHODS OF IMAGE SEGMENTATION BASED ON REMOTE SENSING APPLICATIONS

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 1 | Issue 6 | November 2014 ISSN (online): 2349-6010

A Review on Various Methods of Image Segmentation Based on Remote Sensing Applications G. Priyadharsini PG Scholar Department of Information Technology SNS College of Technology, Coimbatore

C.Senthil Kumar Assistant Professor Department of Information Technology SNS College of Technology, Coimbatore

M.Udhayamoorthy Assistant Professor Department of Information Technology SNS College of Technology, Coimbatore

Abstract Information extraction in high-spatial resolution imagery has been the idea of many researchers all around the world. Several methods are used to inherit information from remote sensing data. The arrival of high-spatial resolution imagery necessitates new refined image processing algorithms for varied remote sensing applications such as segmentation of various regions in image.

Image segmentation is defined as the process of isolating an image into non-overlapping and homogenous regions which is a necessary step toward higher level image processing namely automatic image interpretation, image analysis etc. Its performance decides the concluding result of a computer visual task. This paper surveys various methods which developed recently used for Remote sensing image segmentation. Each method is differentiated with other surveyed method and comparative measures of methods are presented which provides the merits and demerits of various image segmentation methods.

Keywords: Image Segmentation, Merging, Multiscale Segmentation, Region-Based Image Segmentation, Remote Sensing Image, Statistical Region Merging. _______________________________________________________________________________________________________

I. INTRODUCTION Systems are sufficient for Remote Sensing Image (RSI) because of following With the evolution of remote sensing satellite image technology, the larger growth in the spatial resolution of remote sensing image is explored. Exploring such a efficient and faster information extraction methods and high-resolution remote sensing image processing has turn into an significant research theme in remote sensing applications. Segmentation of image and inheriting exact regions of interest is the primary stage to automatic extraction of ground objects in an image by computer vision system, and act as the foundation of articulating and measuring ground objects. Consequently, image segmentation has grown to be one of the chief researches that inheriting ground targets in the images of high resolution remote sensing application based system. Still, due to the enormous data and composite details of applications of high-resolution remote sensing image, the method of segmentation is altered from the usual natural images.

Remote sensing image segmentation is a method to segregate an image into homogenous regions and to identify interested regions of objects, which is an important step toward advanced stage image processing. Since remote sensing images are multispectral, multi sensor and multi resolution, they enclose shape, spectrum, texture and various characteristics information. The redundancy and complexity are increased significantly so that common image segmentation techniques cannot attain acceptable results. Recently, the techniques based on the Theory of Machine Learning have got serious attentions in the areas such as local filtering, region growing, and so on. These techniques or methods no longer observes exact solution for image segmentation process, instead these process seeks for improved approximation of the exact results. These systems do not rely on particular part information to establish most favourable solutions, so they are more appropriate for remote sensing image segmentation.Yet, not all the segmentation particulars.  The redundancy and complexity of the Remote Sensing Image increases considerably.  The RSI offers more information namely context shape, spectral and texture.  The RSI is precisely bothered by noises, illuminance and so on.

The following literature surveys various methods for Remote sensing image segmentation. And merits and demerits of each method are represented in the comparative table which is described in the following section.

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A Review on Various Methods of Image Segmentation Based on Remote Sensing Applications (IJIRST/ Volume 1 / Issue 6 / 031)

II. DISCRETE METHODS FOR REMOTE SENSING IMAGE SEGMENTATION A. Multiscale Segmentation Approach In [1] Jianyu et.al presented edge-guided Multi scale segmentation which achieves selection of unsupervised scale in objectbased examination. This method incorporates four major execution steps. The merge practice of two adjacent regions in Multi scale segmentation is limited by an extra state of the power of edge information among them. The performance of this method was experimentally confirmed in applications of coastal remote sensing. The result of this method reproduces the valid structure of scale distribution of ground objects in composite areas namely an extremely fragmented agro-waterfront landscape. This effectively evades the limitation that presents in one definite scale segmentation result. Edge information is estimated subsequent to the application of the canny edge detector on multispectral imagery enlarged from monochrome edge detection. Since edge achieves a protective role in the segmentation method as an alternative for scale, the best scale selection in multistage investigation is diminished to the procedure of edge detection.

B. Extreme Learning Machines In [2] Ramón Moreno presents the application of Extreme Learning Machines (ELM) for the classification of remote sensing hyper spectral data. The definite goal of the work is to attain correct thematic maps of soybean crops, which have confirmed to be hard to recognize by automated measures. The classification procedure processed is as follows: Initially, spectral data is converted into a hyper-spherical demonstration. Secondly, a strong image gradient is calculated over the representation of hyperspherical image which permitting an image segmentation that recognizes major crop schemes. Third, greedy wrapper approach is used for performing feature selection approach. At last, training and testing of classifier is done based on the selected image pixel features for robust segmentation and classification process respectively.

C. Statistical region growing and hierarchical merging In [3] Carvalho et.al presented statistical region growing and hierarchical merging for segmentation of remote sensing based application images. This work presents a method to carry out hierarchical image segmentation employing the noisy image as input data, without filtering process and a better common approach to contract with any structural size of image. This type of approach separates a speckled image into a enormous number of homogeneous small regions by employing a procedure of statistical region growing which is merged with the coefficient of variation investigation. The algorithm of statistical region growing generates an original division of segments that are displayed in a region of adjacency graph to be forward developed by the hierarchical merging method. The combination of hierarchical stepwise optimization (HSWO) algorithm with Kolmogorov– Smirnov hypothesis test is done by the merging process. The earlier method organizes the region merging with a segmented features are merged as the cost function and the latter achieves a premise test to successful merges the pair of segments, chosen in the adjacency graph of hierarchical region.

D. Local region-based level set segmentation method In [4] Lingfeng et.al presented new local region-based level set segmentation method for remote sensing based image applications. The key contribution of this method is to present the LSD energy that not only represents local separability, but also attains global uniformity. Thus, this method extracts the merits of both local and global methods, i.e. the robustness to initial contours, the capability of administrating image in homogeneity and the low computational cost. The presented method has been assessed on an image quantity with intensity in homogeneities.

E. Local region-based Chan–Vese model In [5] Shigang et.al presented Local region-based Chan–Vese model for segmentation of image. This method considers the local characteristics of image and models the segmented images effectively and efficiently with intensity inhomogeneity. In order to diminish the dependence on physical initialization in various active contour models, a degraded CV approach is presented for automatic segmentation. The result from this type of automatic segmentation takes from the initial contour of the Local regionbased Chan–Vese model. Additionally, the level set function is regularized by employing Gaussian filtering which smooth in the process of evolution.

F. Statistical Region Merging and Nonlinear Diffusion In [6] Xiaotao et.al presented the statistical region merging (SRM) model to segment remote sensing images. In addition, according to the description and faults of SRM, the improved model is obtained as follows: Initially, the information of image gradient is included in the sort function that can increase the distinctions among regions. Secondly, the SRM is combined with the nonlinear diffusion that can guard borders, then the needs of regional homogeneity are enhanced, and then the model's antinoise is also reinforced. Finally, the concern of SRM has over integration defect, that produces a predicate, in which the over merging regions are selected and then segmented by means of integrated active contour model ( IAC).

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A Review on Various Methods of Image Segmentation Based on Remote Sensing Applications (IJIRST/ Volume 1 / Issue 6 / 031)

G. Automatic region-based image segmentation In [7] Zhongwu et.al presented automatic Region-based Image Segmentation Algorithm founded on kmeans clustering (RISA), particularly considered for remote sensing applications. This method incorporates five steps such as clustering by using k-means, Initialization of segment, Generation of seed, region growing, and region merging approach. Region-based Image Segmentation Algorithm founded on kmeans clustering (RISA) was estimated by means of a case study concentrating on land-cover classification for two positions. The two areas include a residential region in Fresno, CA and an agricultural region in the Republic of South Africa. RISA produced extremely homogeneous regions anchored in visual inspection. Higher accuracy is obtained with the land-cover classification by employing the RISA-derived image segments rather than the classifications using the images from the Define software namely cognition and unique image pixels in grouping with a least distance classifier.

H. Watershed Partition and DCT Energy Compaction In [8] Chi-Man Pun presented image segmentation approach by improved watershed partition and DCT energy compaction approach. The presented energy compaction articulates the local texture of a region of image which is attained by exploring the discrete cosine transform. The approach is said to be a hybrid segmentation technique which is arranged of three phases. First, the watershed transform is exploited by using techniques of pre processing. In order to split the images into various disjoint patches, edge detection and marker is used with three features such as mean, variance and region size which are utilized to compute region energy for grouping purpose. In the second merging phase, the DCT transform is employed for energy compaction which is a principle for comparison of texture and region merging. At last the original image can be segmented into numerous partitions of images accordingly.

III. COMPARATIVE ANALYSIS The following comparative table provides the merits and demerits of each surveyed method as follows: S.No

Title

1

Edge-Guided Multiscale Segmentation of Satellite Multispectral Imagery

2

Extreme learning machines for soybean classification in remote sensing hyper spectral images

Table -1 Comparison of Algorithms Technique Merits

Demerits

Multiscale Segmentation

Edge details are preserved with segmentation result

Complexity occurs during segmentation of fine structured area

Extreme learning machines

Better performance and improved result is obtained

Degrades classification accuracy by not considering deeper study in FDA spectra representations

Statistical region growing and hierarchical merging

Achieved better segmentation result in SAR image

Does not investigated the methods to provide initial Image partition.

3

SAR imagery segmentation by statistical region growing and hierarchical merging

4

Region-based image segmentation with local signed difference energy”

Local region-based level set segmentation method

Achieves the ability of handling image in homogeneity, the robustness to initial contours and the low computational cost

Cannot segment the Bright and dark targets in image

5

A local region-based Chan– Vese model for image segmentation

local region-based Chan–Vese model

Computationally efficient and much less sensitive to the initial contour.

Fails to segment images with intensity inhomogeneity.

6

Remote Sensing Image Segmentation Based on Statistical Region Merging and Nonlinear diffusion

Statistical Region Merging and Nonlinear Diffusion

Handles noise ability and provides better segmentation result

Interior relation between SRM and nonlinear diffusion are not described to determine the number of iterations

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A Review on Various Methods of Image Segmentation Based on Remote Sensing Applications (IJIRST/ Volume 1 / Issue 6 / 031)

7

An automatic region-based image segmentation algorithm for remote sensing applications

8

A Region Based Image Segmentation by Watershed Partition and DCT Energy Compaction

Automatic regionbased image segmentation

Produce multi-scale segmented images, Flexible, Scalable, Reproducible, and Compatible.

Functions to quantitatively assess segmentation is not included

Watershed Partition and DCT Energy Compaction

Good segmentation robustness and efficiency is obtained

Less computational efficient

IV.

CONCLUSION

The review presents various methods used for Segmentation of remote sensing application based images known as high-spatial resolution images. Each surveyed method is significantly efficient in image segmentation process. This paper shows the merits and demerits of each method in various aspects. The efficiency of the surveyed method can be measured in terms of Segmentation accuracy and computational time obtained for each method. The advantages of each method can be taken into account and advanced these techniques can be enhanced for various remote sensing based application images efficiently

REFERENCES Jianyu Chen, Jonathan Li, Delu Pan Qiankun Zhu, and Zhihua Mao,”Edge Guided Multiscale Segmentation of Satellite Multispectral Imagery” IEEE Transactions On Geosciences And Remote Sensing, VOL. 50, NO. 11, NOVEMBER 2012 [2] Ramón Moreno, Francesc Corona, Amaury Lendasse, Manuel Graña, LênioS.Galvão,”Extreme learning machines for soybean classification in remote sensing hyper spectral images” Neurocomputing 128 (2014) 207– 216 [3] E.A. Carvalho , D.M. Ushizima , F.N.S. Medeiros , C.I.O. Martins , R.C.P. Marques ,I.N.S. Oliveira,” SAR imagery segmentation by statistical region growing and hierarchical merging” Digital Signal Processing 20 (2010) 1365–1378 [4] Lingfeng Wang , Huaiyu Wu, Chunhong Pan,” Region-based image segmentation with local signed difference energy”, Pattern Recognition Letters 34 (2013) 637–645 [5] Shigang Liu , YaliPeng,” A local region-based Chan–Vese model for image segmentation”, Pattern Recognition 45 (2012) 2769–2779 [6] Xiaotao Wang, Jitao Wu,” Remote Sensing Image Segmentation Based on Statistical Region Merging and Nonlinear Diffusion”IEEE, 978-1-4244-5194-4, 2010 [7] Zhongwu wang , john r. Jensen , jungho im,” An automatic region-based image segmentation algorithm for remote sensing applications”, Environmental Modelling & Software 25 (2010) 1149-1165 [8] Chi-Man Pun, Ning-Yu An, Miao Cheng,” A Region-Based Image Segmentation by Watershed Partition and DCT Energy Compaction”, IEEE, eight international conference 978-0-7695-4484-7, 2011 [9] S. Jianbo, J. Malik, Normalized cuts and image segmentation, IEEE Trans. PatternAnal. Mach. Intell. 22 (2000) 888–905 [10] P.F. Felzenszwalb, D.P. Huttenlocher, Efficient graph-based image segmentation, Int. J. Comput. Vision 59 (2004) 167–181 [11] S. Wang, J.M. Siskind, Image segmentation with ratio cut, IEEE Trans. PatternAnal. Mach. Intell. 25 (2003) 675–690 [1]

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