scholarly journals A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2427 ◽  
Author(s):  
Han Gao ◽  
Yunwei Tang ◽  
Linhai Jing ◽  
Hui Li ◽  
Haifeng Ding
Keyword(s):  
Remote Sensing ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Segmentation Quality

Texture Image Segmentation Based on MRMRF in Contourlet Domain

2012 ◽  
Vol 532-533 ◽  
pp. 732-737
Author(s):  
Xi Jie Wang ◽  
Xiao Fan Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Field Model ◽  
Model Parameters ◽  
Texture Image ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Texture Image Segmentation

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

scholarly journals A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

2020 ◽  
Vol 12 (18) ◽  
pp. 3005
Author(s):  
Maofan Zhao ◽  
Qingyan Meng ◽  
Linlin Zhang ◽  
Die Hu ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computational Cost ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Parameters Selection ◽  
Object Based ◽  
Geographic Object ◽  
The Difference

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

scholarly journals A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

2020 ◽  
Author(s):  
Yue Ma ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Jin Ben ◽  
Qianqian Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Evaluation System ◽  
Evaluation Method ◽  
Remote Sensing Data ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Hexagonal Grids ◽  
Discrete Grid

With the rapid development of earth observation, satellite navigation, mobile communication and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. Under this circumstance, a new form of spatial data organization emerged-the global discrete grid. This form of data management can be used for the efficient storage and application of large-scale global spatial data, which is a digital multi-resolution the geo-reference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing and application of current spatial data. There are different types of grid system according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, and establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves that the availability hexagonal grid-based remote sensing data of remote sensing images. And among the three sampling methods, the image obtained by the nearest interpolation sampling method has the highest correlation with the original image.

Unsupervised Segmentation of Remote Sensing Images Using FD Based Texture Analysis Model and ISODATA

2019 ◽  
pp. 661-678
Author(s):  
S. Hemalatha ◽  
S. Margret Anouncia
Keyword(s):  
Remote Sensing ◽  
Texture Analysis ◽  
Confusion Matrix ◽  
Texture Feature ◽  
Google Earth ◽  
Analysis Model ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Analysis Technique ◽  
Class Labels

In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential (FD) based texture analysis model and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP (Local Binary Pattern) and NBP (Neighbors based Binary Pattern) by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.

The Earthquake Damage Remote Sensing Automatic Recgnition Based on Texture of Roof Tiles

2014 ◽  
Vol 651-653 ◽  
pp. 1315-1319 ◽  
Author(s):  
Dong Ping Li ◽  
Jun Gong ◽  
Jing Yi Li ◽  
Shan Shan Guo
Keyword(s):  
Remote Sensing ◽  
Evaluation Method ◽  
Rapid Assessment ◽  
Texture Features ◽  
Technology Support ◽  
Remote Sensing Images ◽  
Gis Technology ◽  
Disaster Assessment ◽  
High Resolution Images ◽  
Earthquake Damages

To meet the technical demands of rapid assessment on small and medium earthquake damages, this paper presents the comprehensive disaster evaluation method of on-spot human-computer interaction survey and remote sensing image analysis based on the GIS technology support in the small and medium earthquakes. By making full use of the advantages of existing data, emphasizing on the automatical identification of the unique texture features of small earthquakes with a combination analysis on high resolution images gained from unmanned aerial vehicles (uav) and the seismic damages, the new method results in the rank distribution of earthquakes by gaining the experienced parameter of local small-medium earthquakes based on the analysis of regional characteristics of texture features of remote sensing images. It is concluded that the evaluation method is more accurate and efficient for small and medium earthquake rapid disaster assessment.

Reliable clarity automatic-evaluation method for optical remote sensing images

2015 ◽  
Author(s):  
Bangyong Qin ◽  
Ren Shang ◽  
Shengyang Li ◽  
Baoqin Hei ◽  
Zhiwen Liu
Keyword(s):  
Remote Sensing ◽  
Evaluation Method ◽  
Optical Remote Sensing ◽  
Automatic Evaluation ◽  
Remote Sensing Images
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