Aerosols are an important component of the atmospheric system. Long time-series observations for ... more Aerosols are an important component of the atmospheric system. Long time-series observations for aerosols are essential for examining global climate change and the ecological environment. Based on Google Earth Engine and MODIS MCD19A2 data, we monitored the spatio-temporal dynamic characteristics of the aerosol optical depth (AOD) in Central Asia from 2001 to 2020. The effects of six environmental factors on the AOD distribution were explored using a geographic detector model and analysed in combination with the land-use/land-cover change (LUCC) and desertification in different periods. The results showed that the average multi-year AOD in Central Asia was 0.1442, with insignificant interannual variations. The high-value areas were mainly distributed in the Aral Sea and surrounding areas of the Tarim Basin in Xinjiang, with notable seasonal variations. The evaluation results for the influencing factors showed that the relative humidity and precipitation had a large effect on the spa...
Oil is an important resource for the development of modern society. Accurate detection of oil wel... more Oil is an important resource for the development of modern society. Accurate detection of oil wells is of great significance to the investigation of oil exploitation status and the formulation of an exploitation plan. However, detecting small objects in large-scale and high-resolution remote sensing images, such as oil wells, is a challenging task due to the problems of large number, limited pixels, and complex background. In order to overcome this problem, first, we create our own oil well dataset to conduct experiments given the lack of a public dataset. Second, we provide a comparative assessment of two state-of-the-art object detection algorithms, SSD and YOLO v4, for oil well detection in our image dataset. The results show that both of them have good performance, but YOLO v4 has better accuracy in oil well detection because of its better feature extraction capability for small objects. In view of the fact that small objects are currently difficult to be detected in large-scale...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
An improved scheme for aliased electrocardiogram (ECG) data compression has been constructed, whe... more An improved scheme for aliased electrocardiogram (ECG) data compression has been constructed, where the predictor exploits the correlative characteristics of adjacent QRS waveforms. The twin-R correlation prediction and lifting wavelet transform (LWT) for periodical ECG waves exhibits feasibility and high efficiency to achieve lower distortion rates with realizable compression ratio (CR); grey predictions via GM(1, 1) model have been adopted to evaluate the parametric performance for ECG data compression. Simulation results illuminate the validity of our approach.
Performance analysis of object detection combined with post-processing schemes are challenging es... more Performance analysis of object detection combined with post-processing schemes are challenging especially that the spatial resolution of images is low in wide-area aerial imagery. In this paper, we present the quantitative results of ten object detection algorithms combined with several post-processing schemes including filtered dilation, heuristic filtering, sieving and closing, a three-stage scheme which involves thresholding with respect to area and compactness, and the proposed scheme of median filtering, opening and closing, followed by linear Gaussian filtering with nonmaximum suppression. We verified the sieving and closing as well as the three-stage scheme display better Fβ-score and PASCAL value via four vehicle detection algorithms. We evaluated combinations of ten object detection and segmentation methods with two post-processing schemes by adopting a set of recent evaluation metrics, i.e., Jaccard Index (JI), Fbw measure, the structure similarity measure (SSIM) and the enhanced alignment measure (EAM). Automatic detection outputs are compared with their ground truth in low-resolution aerial datasets. Classified detection results are established on ten algorithms each combined with the selected post-processing schemes. We take two widely used datasets (VIVID and VEDAI) for performance analysis, compare the detections and time cost of each algorithm either without or with the proposed scheme, and verified our approach via replacing either datasets or algorithms. Quantitative evaluation under a set of enhanced measures proves our test with validity, efficiency, and accuracy.
In low-resolution wide-area aerial imagery, object detection algorithms are categorized as featur... more In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the v...
Aerosols are an important component of the atmospheric system. Long time-series observations for ... more Aerosols are an important component of the atmospheric system. Long time-series observations for aerosols are essential for examining global climate change and the ecological environment. Based on Google Earth Engine and MODIS MCD19A2 data, we monitored the spatio-temporal dynamic characteristics of the aerosol optical depth (AOD) in Central Asia from 2001 to 2020. The effects of six environmental factors on the AOD distribution were explored using a geographic detector model and analysed in combination with the land-use/land-cover change (LUCC) and desertification in different periods. The results showed that the average multi-year AOD in Central Asia was 0.1442, with insignificant interannual variations. The high-value areas were mainly distributed in the Aral Sea and surrounding areas of the Tarim Basin in Xinjiang, with notable seasonal variations. The evaluation results for the influencing factors showed that the relative humidity and precipitation had a large effect on the spa...
Oil is an important resource for the development of modern society. Accurate detection of oil wel... more Oil is an important resource for the development of modern society. Accurate detection of oil wells is of great significance to the investigation of oil exploitation status and the formulation of an exploitation plan. However, detecting small objects in large-scale and high-resolution remote sensing images, such as oil wells, is a challenging task due to the problems of large number, limited pixels, and complex background. In order to overcome this problem, first, we create our own oil well dataset to conduct experiments given the lack of a public dataset. Second, we provide a comparative assessment of two state-of-the-art object detection algorithms, SSD and YOLO v4, for oil well detection in our image dataset. The results show that both of them have good performance, but YOLO v4 has better accuracy in oil well detection because of its better feature extraction capability for small objects. In view of the fact that small objects are currently difficult to be detected in large-scale...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
An improved scheme for aliased electrocardiogram (ECG) data compression has been constructed, whe... more An improved scheme for aliased electrocardiogram (ECG) data compression has been constructed, where the predictor exploits the correlative characteristics of adjacent QRS waveforms. The twin-R correlation prediction and lifting wavelet transform (LWT) for periodical ECG waves exhibits feasibility and high efficiency to achieve lower distortion rates with realizable compression ratio (CR); grey predictions via GM(1, 1) model have been adopted to evaluate the parametric performance for ECG data compression. Simulation results illuminate the validity of our approach.
Performance analysis of object detection combined with post-processing schemes are challenging es... more Performance analysis of object detection combined with post-processing schemes are challenging especially that the spatial resolution of images is low in wide-area aerial imagery. In this paper, we present the quantitative results of ten object detection algorithms combined with several post-processing schemes including filtered dilation, heuristic filtering, sieving and closing, a three-stage scheme which involves thresholding with respect to area and compactness, and the proposed scheme of median filtering, opening and closing, followed by linear Gaussian filtering with nonmaximum suppression. We verified the sieving and closing as well as the three-stage scheme display better Fβ-score and PASCAL value via four vehicle detection algorithms. We evaluated combinations of ten object detection and segmentation methods with two post-processing schemes by adopting a set of recent evaluation metrics, i.e., Jaccard Index (JI), Fbw measure, the structure similarity measure (SSIM) and the enhanced alignment measure (EAM). Automatic detection outputs are compared with their ground truth in low-resolution aerial datasets. Classified detection results are established on ten algorithms each combined with the selected post-processing schemes. We take two widely used datasets (VIVID and VEDAI) for performance analysis, compare the detections and time cost of each algorithm either without or with the proposed scheme, and verified our approach via replacing either datasets or algorithms. Quantitative evaluation under a set of enhanced measures proves our test with validity, efficiency, and accuracy.
In low-resolution wide-area aerial imagery, object detection algorithms are categorized as featur... more In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the v...
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