An Improved Hybrid Segmentation Method for Remote Sensing Images

Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers’ attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran’s index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).

[1]  Peijun Li,et al.  A new segmentation method for very high resolution imagery using spectral and morphological information , 2015 .

[2]  Pedro Gómez Vilda,et al.  An improved watershed algorithm based on efficient computation of shortest paths , 2007, Pattern Recognit..

[3]  Luisa Verdoliva,et al.  Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[5]  Ruzena Bajcsy,et al.  Segmentation of range images as the search for geometric parametric models , 1995, International Journal of Computer Vision.

[6]  Xuezhi Feng,et al.  Optimal Gabor filter-based edge detection of high spatial resolution remotely sensed images , 2017 .

[7]  He Guo,et al.  Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation , 2017, Neurocomputing.

[8]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[9]  Marcin Ciecholewski,et al.  River channel segmentation in polarimetric SAR images: Watershed transform combined with average contrast maximisation , 2017, Expert Syst. Appl..

[10]  Mohammad Hammoudeh,et al.  Information extraction from sensor networks using the Watershed transform algorithm , 2015, Inf. Fusion.

[11]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Xin Tong,et al.  Cloud Extraction from Chinese High Resolution Satellite Imagery by Probabilistic Latent Semantic Analysis and Object-Based Machine Learning , 2016, Remote. Sens..

[13]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[14]  Muguo Li,et al.  Study of fluid edge detection and tracking method in glass flume based on image processing technology , 2017, Adv. Eng. Softw..

[15]  Jin Xu,et al.  Detection and Monitoring of Oil Spills Using Moderate/High-Resolution Remote Sensing Images , 2017, Archives of Environmental Contamination and Toxicology.

[16]  Max Mignotte,et al.  EFA-BMFM: A multi-criteria framework for the fusion of colour image segmentation , 2017, Inf. Fusion.

[17]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[18]  Shuicheng Yan,et al.  Learning With $\ell ^{1}$-Graph for Image Analysis , 2010, IEEE Transactions on Image Processing.

[19]  Paul Müller,et al.  Parallel Volume Image Segmentation with Watershed Transformation , 2009, SCIA.

[20]  Aggelos K. Katsaggelos,et al.  Hybrid image segmentation using watersheds and fast region merging , 1998, IEEE Trans. Image Process..

[21]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Luo Guo,et al.  A comparative study of the segmentation of weighted aggregation and multiresolution segmentation , 2016 .

[23]  Paolo Gamba,et al.  Multi-feature combined cloud and cloud shadow detection in GF-1 WFV imagery , 2016, ArXiv.

[24]  Li Feng,et al.  Adaptive Scale Selection for Multiscale Segmentation of Satellite Images , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Michael G. Sideris,et al.  Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach , 2017, Journal of Geodesy.

[27]  L. Joshua Leon,et al.  Watershed-Based Segmentation and Region Merging , 2000, Comput. Vis. Image Underst..

[28]  Ying Liu,et al.  Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods , 2012 .

[29]  Azriel Rosenfeld,et al.  Compact Region Extraction Using Weighted Pixel Linking in a Pyramid , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Juan Du,et al.  An object-oriented daytime land-fog-detection approach based on the mean-shift and full lambda-schedule algorithms using EOS/MODIS data , 2011 .

[31]  Zhengqiang Li,et al.  In-Flight Calibration of GF-1/WFV Visible Channels Using Rayleigh Scattering , 2017, Remote. Sens..

[32]  Yun Zhang,et al.  Region based segmentation of QuickBird multispectral imagery through band ratios and fuzzy comparison , 2009 .

[33]  Antônio Miguel Vieira Monteiro,et al.  Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation , 2006 .

[34]  Mingquan Wu,et al.  Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring , 2015, International journal of environmental research and public health.

[35]  K. Moffett,et al.  Distinguishing wetland vegetation and channel features with object-based image segmentation , 2013 .

[36]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Jing-Yu Yang,et al.  A fast watershed algorithm based on chain code and its application in image segmentation , 2005, Pattern Recognit. Lett..

[38]  Nengcheng Chen,et al.  Topology Adaptive Water Boundary Extraction Based on a Modified Balloon Snake: Using GF-1 Satellite Images as an Example , 2017, Remote. Sens..

[39]  Brian A. Mikelbank Quantitative Geography: Perspectives on Spatial Data Analysis, by A. S. Fotheringham, C. Brunsdon, and M. Charlton , 2010 .

[40]  Liangpei Zhang,et al.  An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Mei Xie,et al.  Illumination normalization based on correction of large-scale components for face recognition , 2017, Neurocomputing.

[42]  Shaun Quegan,et al.  Quantitative comparison of the performance of SAR segmentation algorithms , 1998, IEEE Trans. Image Process..

[43]  Alina N. Moga,et al.  An efficient watershed algorithm based on connected components , 2000, Pattern Recognit..

[44]  Alain Trémeau,et al.  Regions adjacency graph applied to color image segmentation , 2000, IEEE Trans. Image Process..