scholarly journals Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery

2016 ◽  
Vol 9 (1) ◽  
pp. 15 ◽  
Author(s):  
Xiong Xu ◽  
Xiaohua Tong ◽  
Antonio Plaza ◽  
Yanfei Zhong ◽  
Huan Xie ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Mapping Model ◽  
Remotely Sensed Imagery ◽  
Endmember Variability ◽  
Pixel Mapping

scholarly journals A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery

2019 ◽  
Vol 8 (10) ◽  
pp. 464
Author(s):  
Shimin Fang ◽  
Xiaoguang Zhou ◽  
Jing Zhang
Keyword(s):  
Information Content ◽  
Structural Characteristics ◽  
Remotely Sensed ◽  
Mapping Model ◽  
Single Level ◽  
Remotely Sensed Imagery ◽  
Mapping Strategy ◽  
Remotely Sensed Images ◽  
The Difference ◽  
Mapping Models

Considering the multiscale characteristics of the human visual system and any natural scene, the spatial autocorrelation of remotely sensed imagery, and the multilevel spatial structure of ground targets in remote sensing images, an information-measurement approach based on a single-level geometrical mapping model can only reflect partial feature information at a single level (e.g., global statistical information and local spatial distribution information). The single mapping model cannot validly characterize the information of the multilevel and multiscale features of the spatial structures inherent in remotely sensed images. Additionally, the validity, practicability, and application range of the results of single-level mapping models are greatly limited in practical applications. In this paper, we present the multilevel geometrical mapping entropy (MGME) model to evaluate the information content of related attribute characteristics contained in remotely sensed images. Subsequently, experimental images with different types of objects, including reservoir area, farmland, water area (i.e., water and trees), and mountain area, were used to validate the performance of the proposed method. Experimental results show that the proposed method can not only reflect the difference in the information of images in terms of spectrum features, spatial structural features, and visual perception but also eliminates the inadequacy of a single-level mapping model. That is, the multilevel mapping strategy is feasible and valid. Additionally, the vector set of the MGME method and its standard deviation (Std) value can be used to further explore and study the spatial dependence of ground scenes and the difference in the spatial structural characteristics of different objects.

A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery

2018 ◽  
Vol 56 (11) ◽  
pp. 6763-6778 ◽  
Author(s):  
Xiong Xu ◽  
Xiaohua Tong ◽  
Antonio Plaza ◽  
Jun Li ◽  
Yanfei Zhong ◽  
...  
Keyword(s):  
Hyperspectral Imagery ◽  
Remotely Sensed ◽  
Mapping Model ◽  
Pixel Mapping

Remotely sensed imagery revealing the effects of hurricanes Gustav and Ike on coastal Louisiana

Data Series ◽  
10.3133/ds566 ◽  
2010 ◽  
Author(s):  
John A. Barras ◽  
John C. Brock ◽  
Robert A. Morton ◽  
Laurinda J. Travers
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Imagery ◽  
Coastal Louisiana

Map-guided interpretation of remotely-sensed imagery

1980 ◽  
Author(s):  
J. M. Tenenbaum ◽  
H. G. Barrow ◽  
R. C. Bolles ◽  
M. A. Fischler ◽  
H. C. Wolf
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Imagery
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