Visual attention model based mining area recognition on massive high-resolution remote sensing images

2015 ◽  
Vol 18 (2) ◽  
pp. 541-548 ◽  
Author(s):  
Xiaolu Song ◽  
Guojin He ◽  
Zhaoming Zhang ◽  
Tengfei Long ◽  
Yan Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Attention ◽  
Mining Area ◽  
Remote Sensing Images ◽  
Visual Attention Model ◽  
Attention Model ◽  
Model Based

scholarly journals An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images

2019 ◽  
Vol 11 (8) ◽  
pp. 987 ◽  
Author(s):  
Yan Peng ◽  
Zhaoming Zhang ◽  
Guojin He ◽  
Mingyue Wei
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
High Resolution ◽  
Visual Attention ◽  
Object Oriented ◽  
Mining Area ◽  
Remote Sensing Images ◽  
Visual Attention Model ◽  
Attention Model ◽  
Mining Areas

An improved GrabCut method based on a visual attention model is proposed to extract rare-earth ore mining area information using high-resolution remote sensing images. The proposed method makes use of advantages of both the visual attention model and GrabCut method, and the visual attention model was referenced to generate a saliency map as the initial of the GrabCut method instead of manual initialization. Normalized Difference Vegetation Index (NDVI) was designed as a bound term added into the Energy Function of GrabCut to further improve the accuracy of the segmentation result. The proposed approach was employed to extract rare-earth ore mining areas in Dingnan County and Xunwu County, China, using GF-1 (GaoFen No.1 satellite launched by China) and ALOS (Advanced Land Observation Satellite) high-resolution remotely-sensed satellite data, and experimental results showed that FPR (False Positive Rate) and FNR (False Negative Rate) were, respectively, lower than 12.5% and 6.5%, and PA (Pixel Accuracy), MPA (Mean Pixel Accuracy), MIoU (Mean Intersection over Union), and FWIoU (frequency weighted intersection over union) all reached up to 90% in four experiments. Comparison results with traditional classification methods (such as Object-oriented CART (Classification and Regression Tree) and Object-oriented SVM (Support Vector Machine)) indicated the proposed method performed better for object boundary identification. The proposed method could be useful for accurate and automatic information extraction for rare-earth ore mining areas.

Based on High Resolution of Remote Sensing Data Mining Houses Information Extraction Methods Research

2012 ◽  
Vol 170-173 ◽  
pp. 2803-2807
Author(s):  
Yan Hua Sun ◽  
Ping Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Spatial Resolution ◽  
Mining Area ◽  
Extraction Methods ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Extraction Technology ◽  
Solar Altitude

High resolution remote sensing images generally refer to image to the spatial resolution within 10m aerospace、aviation remote sensing images. The emergence of high-resolution images strengthened the ability to recognize the large scale features, especially for the extraction of houses information in mining area. High spatial resolution image has rich delicate texture feature, it is urgent to solution the problem of how to extract the features. The technology is very useful for statistic houses information、village relocation assessment and research of pressure coal status, providing important data basis for village relocation, statistics, assessment. Taking henan as a mining area for example, houses information extraction methods are discussed. This paper mainly research contents as followings: It is combined with the space texture information of high resolution imaging rich, using different methods to extract building information, including followings: First, ordinary image segmentation technology; this method is simple and feasible, but extracted housing information is not accurate. Second, the object-oriented method of feature extraction technology, visualization degree and extracting accuracy of this method is higher; Third, it has conducted the preliminary height extraction of the houses; according to the solar altitude angles and the shadow of the houses to calculate the height of the houses. And considering the influence of undulating terrain, using the terrain DEM data to analyze study area, finally determined the shadow length, and then used solar altitude angles to calculate houses height. Based on the verification, accuracy evaluation results show that houses contour information extraction accuracy is: accuracy of the number and area is over 80%, the total rate of wrong classifications is lower. Houses highly information extraction accuracy is within the 85%. The research methods are effective.

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