Multiresolution segmentation of video objects in the compression domain

2007 ◽  
Vol 46 (9) ◽  
pp. 097401 ◽  
Author(s):  
Zhaoyang Zhang
Keyword(s):  
Video Objects ◽  
Multiresolution Segmentation

scholarly journals Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts

2021 ◽  
Vol 13 (13) ◽  
pp. 2618
Author(s):  
Carsten Juergens ◽  
M. Fabian Meyer-Heß
Keyword(s):  
Early Identification ◽  
Satellite Images ◽  
Time Intervals ◽  
Knn Classification ◽  
New Construction ◽  
Multiresolution Segmentation ◽  
Specific Material ◽  
Available Information ◽  
Economic Forecasts ◽  
Very High

This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal “patterns of life” in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Unsupervised Segmentation of Stereoscopic Video Objects: Constrained Segmentation Fusion Versus Greedy Active Contours

2014 ◽  
Vol 81 (2) ◽  
pp. 153-181 ◽  
Author(s):  
Klimis S. Ntalianis ◽  
Anastasios D. Doulamis ◽  
Nikolaos D. Doulamis ◽  
Nikos E. Mastorakis ◽  
Athanasios S. Drigas
Keyword(s):  
Active Contours ◽  
Stereoscopic Video ◽  
Unsupervised Segmentation ◽  
Video Objects

scholarly journals Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

2018 ◽  
Vol 10 (11) ◽  
pp. 1705 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Hossein Rizeei ◽  
Abdinur Abdulle
Keyword(s):  
Noise Removal ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Spectral Difference ◽  
Sar Images ◽  
Thematic Map ◽  
Tropical Regions ◽  
Passive Sensor ◽  
Multiresolution Segmentation ◽  
Salt And Pepper

This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.

Feature-based detection and correction of occlusions and split of video objects

2008 ◽  
Vol 3 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Carlos Vázquez ◽  
Mohammed Ghazal ◽  
Aishy Amer
Keyword(s):  
Video Objects ◽  
Feature Based

scholarly journals Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1193 ◽  
Author(s):  
Yongji Wang ◽  
Qingwen Qi ◽  
Ying Liu
Keyword(s):  
Distance Measures ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
Segmentation Quality ◽  
Two Measures ◽  
Multiresolution Segmentation ◽  
Adjacent Segments

Image segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) method using the area-weighted variance (WV) and Jeffries-Matusita (JM) distance to compare two image partitions to evaluate segmentation quality. The two measures were calculated based on the local measure criteria, and the JM distance was improved by considering the contribution of the common border between adjacent segments and the area of each segment in the JM distance formula, which makes the heterogeneity measure more effective and objective. Then the two measures were presented as a curve when changing the scale from 8 to 20, which can reflect the segmentation quality in both over- and under-segmentation. Furthermore, the WV and JM distance measures were combined by using three different strategies. The effectiveness of the combined indicators was illustrated through supervised evaluation (SE) methods to clearly reveal the segmentation quality and capture the trade-off between the two measures. In these experiments, the multiresolution segmentation (MRS) method was adopted for evaluation. The proposed UE method was compared with two existing UE methods to further confirm their capabilities. The visual and quantitative SE results demonstrated that the proposed UE method can improve the segmentation quality.

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