Landslide Detection Leveraging Spectral-Spatial Correlation Fusion in Aerospace Remote Sensing

2019 ◽  
Author(s):  
Shanjing Chen ◽  
Zongsheng Zhang ◽  
Qing Kang ◽  
Zhiqiang Shen ◽  
Ruochong Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Correlation

scholarly journals A Hybrid Model Integrating Spatial Pattern, Spatial Correlation, and Edge Information for Image Classification

2019 ◽  
Vol 11 (13) ◽  
pp. 1599 ◽  
Author(s):  
Yunwei Tang ◽  
Linhai Jing ◽  
Fan Shi ◽  
Xiao Li ◽  
Fang Qiu
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Correlation ◽  
Hybrid Model ◽  
General Distribution ◽  
Multiple Point ◽  
Spatial Covariance ◽  
Edge Preserving ◽  
Remote Sensing Classification ◽  
Different Sources

This paper develops a novel hybrid model that integrates three spatial contexts into probabilistic classifiers for remote sensing classification. First, spatial pattern is introduced using multiple-point geostatistics (MPGs) to characterize the general distribution and arrangement of land covers. Second, spatial correlation is incorporated using spatial covariance to quantify the dependence between pixels. Third, an edge-preserving filter based on the Sobel mask is introduced to avoid the over-smoothing problem. These three types of contexts are combined with the spectral information from the original image within a higher-order Markov random field (MRF) framework for classification. The developed model is capable of classifying complex and diverse land cover types by allowing effective anisotropic filtering of the image while retaining details near edges. Experiments with three remote sensing images from different sources based on three probabilistic classifiers obtained results that significantly improved classification accuracies when compared with other popular contextual classifiers and most state-of-the-art methods.

scholarly journals Spatial Pattern of Bathymetry in Wangi-Wangi Island and Its Surrounding Based on GEBCO Data and Nautical Map

2016 ◽  
Vol 29 (2) ◽  
pp. 144
Author(s):  
Yulius Yulius ◽  
H L Salim ◽  
M Ramdhan
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Shallow Water ◽  
Spatial Correlation ◽  
Research Area ◽  
Barrier Reef ◽  
Kriging Method ◽  
Gis And Remote Sensing ◽  
Eastern Side ◽  
Gis Technique

The study aims is to define bathymetry based on General Bathymetric Chart of the Oceans (GEBCO) and Nautical Map using GIS technique. The methods used in this study are the kriging method which combines the spatial correlation among the data using GIS and Remote Sensing software. The result shows that bathymetry at research area can be divided into five classes, these are: (1) 0-2 meter with area of 1.797,61 hectare, (2) 2-5 meter with area of 2.059,06 hectare, (3) 5-10 meter with area of 1.184,02 hectare, (4) 10-25 meter with area of 3.025.00 hectare, (5) 25-200 meter with area of 5.648.62 hectare.The spatial pattern of bathymetry dispersed from the shallow water at the edge of beach and more deep at the offshore, except at the eastern side of Wangi-Wangi island which has barrier reef and created basin between them.

scholarly journals A Multi-Level Non-Uniform Spatial Sampling Method for Accuracy Assessment of Remote Sensing Image Classification Results

2020 ◽  
Vol 10 (16) ◽  
pp. 5568
Author(s):  
Zhenhua Wang ◽  
Lizhi Xu ◽  
Qing Ji ◽  
Wei Song ◽  
Lingqun Wang
Keyword(s):  
Remote Sensing ◽  
Spatial Correlation ◽  
Sampling Method ◽  
Sampling Methods ◽  
Accuracy Assessment ◽  
Remote Sensing Image ◽  
Spatial Sampling ◽  
Remote Sensing Images ◽  
Multi Level ◽  
Sample Points

Accuracy assessment of classification results has important significance for the application of remote sensing images, which can be achieved by sampling methods. However, the existing sampling methods either ignore spatial correlation or do not consider spatial heterogeneity. Here, we proposed a multi-level non-uniform spatial sampling method (MNSS) for the accuracy assessment of classification results. Taking the remote sensing image of Kobo Askov, Texas, USA, as an example, the classification result of this image was obtained by Support Vector Machine (SVM) classifier. In the proposed MNSS, the studied spatial region was zoned from high to low resolution based on the features of spatial correlation. Then, the sampling rate of each zone was deduced from the low to high resolution based on the spatial heterogeneity. Finally, the positions of sample points were allocated in each zone, and the classification results of the sample points were obtained. We also used other sampling methods, including a random sampling method (SRS), stratified sampling method (SS), and spatial sampling of the gray level co-occurrence matrix method (GLCM), to obtain the classification results of the sample points (2-m resolution). Five categories of ground objects in the same region were used as the ground truth data. We than calculated the overall accuracy, Kappa coefficient, producer accuracy, and user accuracy to estimate the accuracy of the classification results. The results showed that MNSS was the strictest inspection method as shown by the minimum value of accuracy. Moreover, MNSS overcame the shortcoming of SRS, which did not consider the spatial correlation of sample points, and overcame the shortcomings of SS and GLCM, which had redundant information between sample points. This paper proposes a novel sampling method for the accuracy assessment of classification results of remote sensing images.

Phased Arrays for Radio Astronomy, Remote Sensing, and Satellite Communications

2018 ◽  
Author(s):  
Karl F. Warnick ◽  
Rob Maaskant ◽  
Marianna V. Ivashina ◽  
David B. Davidson ◽  
Brian D. Jeffs
Keyword(s):  
Remote Sensing ◽  
Radio Astronomy ◽  
Phased Arrays ◽  
Satellite Communications

Hyperspectral Imaging Remote Sensing

2016 ◽  
Author(s):  
Dimitris Manolakis ◽  
Ronald Lockwood ◽  
Thomas Cooley
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging
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