Optimal segmentation scale selection and evaluation of cultivated land objects based on high-resolution remote sensing images with spectral and texture features

2021 ◽  
Author(s):  
Heng Lu ◽  
Chao Liu ◽  
Naiwen Li ◽  
Xiao Fu ◽  
Longguo Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Texture Features ◽  
Cultivated Land ◽  
Scale Selection ◽  
Remote Sensing Images ◽  
Optimal Segmentation

scholarly journals CHANGE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES USING LEVENE-TEST AND FUZZY EVALUATION

2018 ◽  
Vol XLII-3 ◽  
pp. 1695-1701
Author(s):  
G. H. Wang ◽  
H. B. Wang ◽  
W. F. Fan ◽  
Y. Liu ◽  
H. J. Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Evaluation Method ◽  
Fault Tolerant ◽  
Texture Features ◽  
Fuzzy Evaluation ◽  
Remote Sensing Images ◽  
Texture Information ◽  
Levene Test

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898 ◽  
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

scholarly journals Research on Multifeature Segmentation Method of Remote Sensing Images Based on Graph Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wenxing Bao ◽  
Xiuhong Yao
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
High Resolution ◽  
Texture Features ◽  
Original Image ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Spectrum Shape ◽  
Segmentation Accuracy ◽  
Matching Degree

According to the characteristics of high-resolution remote sensing (RS) images, a new multifeature segmentation method of high-resolution remote sensing images combining the spectrum, shape, and texture features based on graph theory is presented in the paper. Firstly, the quadtree segmentation method is used to partition the original image. Secondly, the spectrum, shape, and texture weight components are calculated all based on the constructed graph. The matching degree between pixels and the texture is computed similarity. Finally, the ratio cut standards combination of the spectrum, shape, and texture weight components is used for the final segmentation. The experimental results show that this method can obtain more ideal results and higher segmentation accuracy applied to RS image than those traditional methods.

High-Resolution Remote Multi-Spectral Sensing Images Based on Texture Features

2014 ◽  
Vol 687-691 ◽  
pp. 3596-3599 ◽  
Author(s):  
Chen Ming Li ◽  
Li Qin Zhu ◽  
Qiang Wang ◽  
Zhen Sun ◽  
Feng Chen Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Texture Features ◽  
Remote Sensing Images ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Multispectral Sensing ◽  
Different Characteristics ◽  
Spot 5 ◽  
Spectral Sensing

Aiming at the difficulties in the segmentation for high-resolution remote multispectral sensing images, this paper proposed a segmentation approach for remote sensing images based on texture features. The algorithm implemented precipitation watershed transform respectively on the texture images obtained by the different characteristics of GLCM, and then superimposed the two segmentation results, finally completing the image segmentation by using a novel regional consolidation method that combined the texture features. The experiments were implemented on the high-resolution ALOS and SPOT 5 remote sensing images respectively. Compared with the traditional watershed segmentation approach based on gradient information, the experimental results showed that the proposed algorithm can accurately locate the edges of objects, effectively overcome the phenomenon of over-segmentation and under-segmentation, with a higher segmentation accuracy and stability.

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