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The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal) and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

Image segmentation is object oriented and hence useful in high-resolution image analysis. It provides a partitioning of the image into isolated regions, each one representing a different image. In particular, multiscale methods are based on image transformations that reduce image resolution, and they are able to segment objects of different sizes depending on the chosen resolution. From high-resolution images, small details can be detected, while from low-resolution images, larger structures are detected. The multi-scale behavior of image features has been analyzed in different ways. Tracking of intensity extrema along scales defined by Lifshitz [

In this paper, we proposed a new segmentation algorithm for high-resolution remote sensing images, which can also be applied to medical and nonmedical images. We used a bi-orthogonal (bior) wavelet decomposition to describe a remote sensing image in multiple resolutions. A suitable resolution is chosen. The gradient image is estimated (or computed) by the simple grey scale morphology. To avoid over segmentation, we have imposed the selective minima (regional minima of the image) on the gradient image. The watershed transform is applied and the segmentation result is projected to a higher resolution, using the inverse wavelet transform until the full resolution of segmented image is obtained. A general outline of the proposed method is shown in Figure

Flow chart of algorithm.

The wavelet transform is a mathematical tool that can be used to describe images in multiple resolutions. The wavelet transform can be represented with an equation like Fourier transform.

According to Mallat’s pyramidal algorithm [_{1}) of the original image as shown in Figure _{1}) of original image (Figure _{1}) as shown in Figure _{1}) as shown in Figure _{1}) to generate the next level of the decomposition adopting the same process. Second level of decomposed images are shown in Figure _{2}) is shown in Figure

Discrete wavelet transform of an image (single level). L—Low Pass Filter; H—High Pass Filter. LL—Approximation coefficients (High scale, low frequency)—cA_{1}. LH, HL and HH– Detail coefficients (low scale, high frequency)—cH_{1}, cV_{1} and cD_{1}.

Original image size (

First level decomposition of Image: (a) approximation coefficients, (b) horizontal coefficients, (c) vertical coefficients, (d) diagonal coefficients.

cA_{1} (

cH_{1 }(

cV_{1} (

cD_{1} (

Second level decomposition of Image (cA_{1}) : (a) approximation coefficients, (b) horizontal cients, (c) vertical coefficients, (d) diagonal coefficients.

cA_{2 }(

cH_{2 }(

cV_{2 }(

cD_{2} (

The Approximations are related to one another by

The original image is decomposed into different resolutions using wavelet transform explained as above. In this paper resolution of (_{2} (_{2} (_{2} (

The direct application of watershed transforms causes over segmentation; the over segmented images may require further merging of some regions. The decision on such regions to be merged depends upon homogeneity and similarity criteria based on the extended minima transform defined by Soille [

The results of image segmentation on each resolution.

Test image | Scale levels | Selective minima value, H | Number of segments | Computational time (sec) |
---|---|---|---|---|

Satellite image 1 | cA_{0} | 95 | 188 | 0.55 |

cA_{1} | 1000 | 30 | 0.23 | |

cA_{2} | 3000 | 7 | 0.15 | |

Satellite image 2 | cA_{0} | 60 | 215 | 0.75 |

cA_{1} | 1200 | 35 | 0.25 | |

cA_{2} | 3900 | 4 | 0.10 | |

Brain image | cA_{0} | 120 | 62 | 0.45 |

cA_{1} | 500 | 22 | 0.3 | |

cA_{2} | 3500 | 2 | 0.10 | |

Flowers image | cA_{0} | 130 | 69 | 0.40 |

cA_{1} | 800 | 8 | 0.25 | |

cA_{2} | 3000 | 3 | 0.10 | |

Peppers image | cA_{0} | 60 | 38 | 0.35 |

cA_{1} | 1200 | 5 | 0.20 | |

cA_{2} | 2700 | 3 | 0.15 |

Final segmented Images (a) original image and (b) to (d) are the watershed imposed original images from level 2 to level 0 resolution respectively.

Once the segmented image is generated at the

To prevent noise from being introduced back into the unsampled image during Inverse transform, we have used only some detail coefficients (those related to edges). This means that all detail images (horizontal, vertical, diagonal) are set to zero, except those whose position correspond to the watershed lines of image

All the computation involving wavelet transform, edge detection and watershed transform are implemented using MATLAB. Application of the wavelet transform takes very short time, this quick response is mainly due to the property of wavelet decomposition and reconstruction which have fast algorithms. The algorithms are based on convolutions with a bank of filters. We used two different satellite images. Satellite image 1 is a PAN image of IRC-1C (5.8 m resolution). It shows Visakapatnam sea coast with fishing jetties, harbor channels and dense city with concrete structures. Image 2 is a Cartosat-1 satellite PAN image (2.5 m resolution) of Hyderabad city. The final segmentation results are shown in Figure

We also applied our algorithm on different medical and nonmedical images. Figure

Final segmented images on wavelet decomposition from level 2 to level 0. (a) Original image and, (b) to (d) are the watershed imposed original images from low-resolution (

To evaluate the performance of the presented method, simulation was carried out on the sequence of frames of different images. The pyramid image generated by the 2-scale Bior (Bi-orthogonal) wavelet transformation and selective minima imposed on gradient image for watershed segmentation. We evaluated the results of the segmentation of the present method by using three common objective measurements viz., the selective minima, number of segments and computation time at each scale. The full resolution image has more number of regions, longer computation time and lowest minima value where as the low-resolution image has less number of regions, shorter computational time and higher minima value. Each set of the results of image segmentation on each resolution is shown in Table

In this paper, we proposed a new technique to improve the watershed segmentation on remote sensing images based on Discrete Wavelet Transforms. The lower resolution must be chosen in accordance with the size and number of desired objects to avoid under segmentation. The experimental results indicate that the proposed technique performs well for the remote sensing images as well as for medical and low-resolution images too. The full resolution image has more number of regions, longer computation time and lowest minima value where as the low-resolution image has less number of regions, shorter computation time and higher minima value. We would like to extend this work using other soft computing techniques.

The authors thank the All India Council of Technical Education for utilizing the data obtained in the research project for this paper. The authors also thank Andhra Pradesh State Remote Sensing Application Center for providing the high-resolution satellite data. The medical and nonmedical images are downloaded from Google.com/images.