Study on Optimal Segmenting Scale of Grassland in Remote Sensing Objects

2010 ◽  
Vol 171-172 ◽  
pp. 240-245
Author(s):  
Wei Cui ◽  
Qing Qing Li
Keyword(s):  
Remote Sensing ◽  
Fractal Dimension ◽  
New Method ◽  
Remote Sensing Images ◽  
Maximum Area ◽  
Remote Sensing Classification ◽  
Local Variance ◽  
Internal Connections

Determining optimal segmenting scale of remote sensing objects is very important for remote sensing classification and identification. At present, selecting the optimal scales for objects in remote sensing images usually uses two methods, that is, average local variance and maximum area. Due to influences of subjective factors and difficult to implement, this paper proposes a new method based on fractal dimension to solve this problem. This experiment probes certain internal connections between fractal dimension on different scale and optimal segmenting scale of remote sensing objects. It offers a new method for seeking efficient ways to determine optimal scales of different objects in remote sensing images

scholarly journals Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

2019 ◽  
Vol 11 (2) ◽  
pp. 108 ◽  
Author(s):  
Lu Xu ◽  
Dongping Ming ◽  
Wen Zhou ◽  
Hanqing Bao ◽  
Yangyang Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Color Space ◽  
Texture Features ◽  
Rate Of Change ◽  
Parameters Estimation ◽  
Remote Sensing Images ◽  
Image Region ◽  
Local Variance

Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

scholarly journals Dupin's indicatrix: a tool for quantifying periclinal folds on maps

2003 ◽  
Vol 140 (6) ◽  
pp. 721-726 ◽  
Author(s):  
RICHARD J. LISLE
Keyword(s):  
Remote Sensing ◽  
Aspect Ratio ◽  
Principal Curvature ◽  
Quantitative Assessment ◽  
Tangent Plane ◽  
New Method ◽  
Principal Curvatures ◽  
Remote Sensing Images ◽  
Geometrical Figure ◽  
Anticlastic Curvature

The elliptical and hyperbolic outcrop patterns characteristic of periclinal folds can be used to classify structures according to different curvature attributes. Elliptical patterns indicate domal-basinal structures with synclastic curvature, that is, principal curvatures of the same sign. Hyperbolic patterns are diagnostic of anticlastic curvature (saddle-like structures). Such outcrop geometries are geological examples of Dupin's indicatrix, the geometrical figure obtained by sectioning a curved surface on a plane parallel and almost coincident with the tangent plane. The aspect ratio of Dupin's indicatrix is theoretically related to the ratio of the principal curvature values for the part of the structure being considered. This new method allows quantitative assessment of structures on maps and on remote sensing images. Illustrations are given from Wyoming, USA, and Yorkshire, England.

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