scholarly journals Comparison between Object-based Method and Deep Learning Method for Extracting Road Features Using Submeter-grade High-resolution Satellite Imagery

2019 ◽  
Vol 31 (10) ◽  
pp. 3335
Author(s):  
Dong Gook Lee ◽  
Ji Ho You ◽  
Sung Geun Park ◽  
Seung Hyub Baeck ◽  
Hyun Jik Lee
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Learning Method ◽  
Object Based ◽  
High Resolution Satellite Imagery

scholarly journals Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

2020 ◽  
Vol 12 (16) ◽  
pp. 2626 ◽  
Author(s):  
Qingting Li ◽  
Zhengchao Chen ◽  
Bing Zhang ◽  
Baipeng Li ◽  
Kaixuan Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Spatial Scales ◽  
Detection Accuracy ◽  
Single Shot ◽  
Tailings Dams ◽  
High Resolution Satellite Imagery ◽  
Tailings Ponds

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

scholarly journals Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

2010 ◽  
Vol 2 (12) ◽  
pp. 2748-2772 ◽  
Author(s):  
Cerian Gibbes ◽  
Sanchayeeta Adhikari ◽  
Luke Rostant ◽  
Jane Southworth ◽  
Youliang Qiu
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Object Based ◽  
High Resolution Satellite Imagery

scholarly journals Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning

2022 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Zhihao Wei ◽  
Kebin Jia ◽  
Xiaowei Jia ◽  
Pengyu Liu ◽  
Ying Ma ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Ground Truth ◽  
Fine Tuning ◽  
Forest Monitoring ◽  
Learning Method ◽  
Ground Truth Data ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.

scholarly journals Designing an object based rule set for road detection from high resolution satellite imagery

2015 ◽  
Vol 3 (1) ◽  
pp. 77-94
Author(s):  
Maryam Nikfar ◽  
Mohammad Javad Valadan Zoej ◽  
Mehdi Mokhtarzade ◽  
Mahdi Aliyari Shoorehdeli ◽  
◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Road Detection ◽  
Object Based ◽  
Rule Set ◽  
High Resolution Satellite Imagery
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