Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation Parameters for Very High Resolution Remotely Sensed Satellite Imagery
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Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
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Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation Parameters for Very High Resolution Remotely Sensed Satellite Imagery
- 1. Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation Parameters for Very High Resolution Satellite Imagery P. R. Sarkar1 P. Srinivas2 J .Srinivasulu3 D. Sita4 1Department of Avionics Indian Institute of Space Science and Technology 2Head,GRD 3Scientist- ‘F’ 4Scientist - ’E’ Advanced Data Processing Research Institute
- 2. Outline New paradigm in remote sensing, GEOBIA Image Segmentation, the first and critical step Segmentation algorithms Objective of this Study Preparatory work Study area & Quality assessment technique Methodologies Selection of optimal segmentation parameters Estimation of segmentation parameters Existing methods Proposed approach Results Evaluation of segmentation algorithms Evaluation of Automatic Estimation of Segmentation Parameter Technique Conclusions
- 3. Why GEOBIA? Geographic Object Based Image Analysis 1. The latest breed of very high resolution (VHR) commercial satellite imagery (<2.0m) imagery, which has dramatically increased new possibilities in remote sensing and cartographic applications. 2. Pixel based analysis neglects the spatial information of very high resolution images. 3. Several study demonstrated that geographic object based classifier is a significantly better approach than the classical pixel classifier. 4. A dramatic increase in availability of affordable powerful computing systems. 5. Need of the multi-scale approaches in the monitoring, modelling and management of the environment. GEOBIA is the best choice for this.
- 4. Image Segmentation Segmentation is a process of completely partitioning an image into non-overlapping regions based on homogeneity parameters or on differentiation of heterogeneity parameters. Mathematically, it can be defined as follows: if F is the set of all pixels and P( ) is a homogeneity defined on group of connected pixels, then segmentation is a partitioning of the set F into a set of connected regions (S1, S2, ..., Sn) such that:
- 5. Why Segmentation? 1. In GEOBIA, we treat a remotely sensed image as union of meaningful image objects. To extract each image objects, we need to use segmentation techniques. 2. As discussed in slide: 3, considering an image as objects gives better classification accuracy and also opportunity to use spatial information.
- 6. Segmentation Algorithms 1. Mean-shift Segmentation 2. Multi-resolution Segmentation 3. k-means Segmentation 4. Watershed Segmentation
- 7. Mean-shift segmentation Mean-shift algorithm was introduced by Fukunaga and Hostetler in 1975. It is a versatile, non-parametric density gradient estimation for mode finding or clustering procedure. Idea: For each data point, mean shift defines a window around it and computes the mean of the data point. Then it shifts the center of the window to the mean and repeats the algorithm till it converges. After each iteration, we can consider that the window shifts to a more denser region of the dataset.
- 8. Mean-shift segmentation At the high level, we can specify Mean Shift as follows: 1. Fix a window around each data point. 2. Compute the mean of data within the window. 3. Shift the window to the mean and repeat till convergence
- 12. Multi-resolution segmentation 1. It is a bottom-up region merging technique, which starts from one-pixel object. 2. Throughout this clustering process, the underlying optimisation procedure minimizes the weighted heterogeneity nh in the image objects where, n is the number of the pixels of image segment h is the definition of heterogeneity measure. 3. The increment of heterogeneity (f) is calculated before the merging of two adjacent object happens. 4. No further fusion takes place when the increment in heterogeneity exceeds a threshold or scale.
- 14. k-means segmentation This algorithm has been the most popular unsupervised learning algorithm which is widely used for image segmentation. The k-means clustering algorithm is as follows: 1. Initialize the cluster centroids µ1, µ2, ..., µk randomly. 2. Repeat till convergence
- 15. Watershed segmentation 1. The watershed transform is a region based segmentation approach. 2. Imagine the landscape is being immersed in a lake, with holes pierced in topical minima. The process of filling water is stopped when water reaches the highest peak in the landscape. This process finally gives a portioned region or basins separated by dams also called watershed lines. 3. Flow diagram of this algorithm is given in the next slide.
- 17. Objective of this Study
- 18. Objective of this study 1. Study of various segmentation algorithms and deciding which algorithm works better than the existing techniques for different h-resolution terrain surfaces. 2. Comparing the quality of the segmentation algorithms for very high spatial multi-spectral remotely sensed satellite imagery. 3. Automatic estimation of optimal parameter for segmentation algorithms
- 19. Study Area & Quality Assessment Technique
- 20. Study area The study area analysed for this research downloaded from Digitalglobe website. Figure: Main study area with 16384 × 16384 resolution
- 21. Regions of study Rural area Urban area Forest area Residential area
- 22. Quality assessment technique 1. Visual assessment is taken into account for quality checking of segmentation techniques. 2. In this paper, we decided upon visual assessment on the human eye is considered as strong and experienced source for evaluation of segmentation techniques
- 23. Selection of the Optimal Segmentation Parameters
- 24. Segmentation algorithms and parameters in ORFEO Mean-shift environment Watershed environment
- 25. Segmentation algorithm and parameters in eCogniton Parameters in eCognition
- 26. Segmentation algorithms and parameters in Matlab 1. Mean-shift algorithm has only one bandwidth parameter. 2. k-means has only class parameter. 3. Watershed algorithm does not have any parameter.
- 27. Parameter selection for mean-shift segmentation We used spatial profile to select optimal parameters Spatial profile in horizontal axis
- 28. Spatial profile in vertical axis
- 29. 1. From these spatial profiles we can notice peaks in all bands which indicates transition between two different classes. The range between the pixel positions at left side maximum value and right side maximum value, is taken as range radius. 2. Spatial radius is taken by a trial-and-error method. 3. The other three parameters are taken as constants for all the images under consideration. 4. The optimal parameters are chosen based on visual assessments. The windowing method is described in the next slide.
- 30. Parameter selection at different window size
- 31. Optimal output of mean-shift Parameter selection at 761 X 577 Parameter selection at 780 x 577
- 32. Optimal output of multi-resolution WinSize = 128 X 128 WinSize = 352 X 352 WinSize = 468 X 468 WinSize = 212 X 212
- 33. Parameter selection for k-means and watershed segmentation k-means: 1. The only parameter here is class. Selection of proper class is not difficult when the test image is small, but it produce slots of ambiguity when it comes for big remote images. 2. One approach to learn the class is to use Elbows method. Watershed segmentation: The two parameters for watershed segmentation are chosen based on visual inspection.
- 34. Estimation of the Optimal Segmentation Parameters
- 35. Existing methods Post-estimation of segmentation parameters: 1. Kim et. al has used the concept of local variance to determine optimal segmentation parameters of alliance-level forest classification. 2. Dragut et. al used local variance in image object level and extended it into multi- scale analysis based on single layer and later in multiple layers. 3. Zhao et. al has used the rate of change of local variance which is similar to the ESP tool, to estimate the optimal parameter. 4. Emary et. al used a similarity measure between two image objects in set of successive scales. 5. Karl and Maurer et. Al have used semi-variogram based spatial dependency prediction. Pre-estimation of segmentation parameters: Ming et. al applies spatial statistics to select optimal parameters for mean- shift segmentation algorithm.
- 37. Input features extraction Calculation of spatial statistics before feeding in BPNN
- 38. Training of the BPNN Training BPNN for estimating parameters
- 40. Results
- 41. Evaluation of Segmentation Algorithms
- 42. Rural area Multi-resolution in eCognition Mean-shift in ORFEO Mean-shift in Matlab K-means in Matlab Watershed in Matlab Multi-resolution parameters Mean-shift parameters
- 43. Residential area Multi-resolution in eCognition Mean-shift in ORFEO Mean-shift in Matlab K-means in Matlab Watershed in Matlab Multi-resolution parameters Mean-shift parameters
- 44. Urban area Multi-resolution in eCognition Mean-shift in ORFEO Mean-shift in Matlab K-means in Matlab Watershed in Matlab Multi-resolution parameters Mean-shift parameters
- 45. Forest area Multi-resolution in eCognition Mean-shift in ORFEO Mean-shift in Matlab K-means in Matlab Watershed in Matlab Multi-resolution parameters Mean-shift parameters
- 46. Evaluation of Automatic Estimation of Segmentation Parameter Technique
- 47. Mean Square Error plot for the Trained Network
- 48. Average local variance v/s band for four regions of study
- 49. Test Images Rural area Residential area Urban area Forest area
- 50. Estimation of Segmentation Parameters of Multi-resolution Rural area Residential area Urban area Forest area
- 51. Estimation of Segmentation Parameters of Multi-resolution Rural area Residential area Urban area Forest area
- 52. Conclusions
- 53. Assessment of Segmentation Algorithm • we see that the watershed algorithm is poor. • k-means gives a good segmentation solution though it is affected by salt and paper noise due to within-field variability. • Multi-resolution segmentation is now becoming very famous due to its robust nature and fast processing using multi-threading. From the outcomes, we see that it gives very good segmentation in object level. • Mean-shift segmentation is also a multi-scale segmentation like multi- resolution, but it take a huge amount of time to process in both ORFEO and Matlab.
- 54. Comparison between multi-resolution and mean-shift segmentation
- 55. Automatic Estimation of the Optimal Segmentation Parameters Advantages • The proposed method is a pre-estimation method while other studies are related to post-estimation. Only one approach is there which does pre- estimation, but it only limits its scale selection to certain segmentation algorithm. • This proposed approach is not limited to any particular segmentation algorithm. It can estimate any number of parameters for any segmentation algorithm. • Different remote sensing analysis vendors provide different implementation of segmentation algorithms. This approach tries to map between any VHR landcover and optimal segmentation parameter. • Geo-statistics of every band is being considered which makes it more robust to any multi-spectral data.
- 56. Limitations • Training the BPNN network is a difficult task. Discussion about BPNN network is beyond of the scope of this project. • Calculation of local variance in multiple scale takes a long time. Due to the advancement of high performance computing this limitation can be overcome. • The most important disadvantage is to make a training set for learning the network. Initial human effort is needed but once the network is learnt, the automation process is done