A batch sliding window method for local singularity mapping and its application for geochemical anomaly identification

Fan, Xueli; Chen, Zhijun; Chen, Jianguo; Zhou, Yongzhang

doi:10.1016/j.cageo.2015.11.001

Cited by 40 publications

(11 citation statements)

References 33 publications

(48 reference statements)

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“…Given the disadvantages of traditional image temperature density segmentationneglect of the local variation in the spatial distribution of thermal environment, and empirical determination of the segmentation threshold by operatorsis likely to be time-intensive and results in uncertainty. Therefore, a sliding window method based on the multifractal theory, proposed originally by Li and Cheng, has been widely applied for geochemical anomaly identification . Similar to geochemical material accumulation, the complex thermal diffusion phenomena in a small space can be characterized using the multifractal model in remotely sensed images …”

Section: Materials and Methodsmentioning

confidence: 99%

“…As the square window slides from one position to another, the thermal anomaly index is calculated for every center pixel. According to associated studies, ,, the spatial distribution of the thermal anomaly index is often characterized by the fractal dimension k , which implies that in a two-dimensional raster map, if k ≈ 2, no thermal anomalies exist. If k < 2, a positive thermal anomaly (high temperature) is indicated, and if k > 2, a negative thermal anomaly is indicated (low temperature).…”

Section: Materials and Methodsmentioning

confidence: 99%

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Satellite-Based Detection and Characterization of Industrial Heat Sources in China

Zhang

Yuan

Sun

et al. 2019

Environ. Sci. Technol.

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Despite their important contribution to the economic domain, active heat-releasing industrial plants have significant implications for human health and climate change. However, a spatially detailed dataset of various heat-releasing industrial sectors and large-scale characterization of heat emissions from industrial sources have not been reported yet. In this study, a dataset of heat-releasing industries was established using a national detection map of thermal anomalies produced by a novel and more accurate method employing daily nighttime visible infrared imaging radiometer suite thermal infrared images corresponding to 1 year. Subsequently, we quantified the dimensional features of heat radiation fluxes of China's industrial plants. A total of 12 114 industrial objects were structured in a two-level hierarchical dataset of heat-releasing industries, representing a magnitude of at least 1 order higher than the number enumerated in the state-of-the-art inventory of industrial heat sources across China. The satellite observations helped more completely characterize industrial heat plumes, which represent the industrial heat radiation fluxes with higher levels of densities that prevail in the central-eastern part of China having spatial clustering islands. Our results could be used to inform policy and environmental management in relation to meaningful dynamic industrial supervision, targeting extreme polluters and differentiated emission mitigation measurements.

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Satellite-Based Detection and Characterization of Industrial Heat Sources in China

Zhang

Yuan

Sun

et al. 2019

Environ. Sci. Technol.

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“…In practice, more details should be considered. First, the size of the window needs to ensure that there are enough samples in the window (Xiao et al, 2016), and that the values of the possible missing variables in the window are sufficiently similar so that they can be ignored (Koutsias et al, 2010;Zhang et al, 2018b). The local window size was finally determined as 201 cells × 201 cells, that is, 18 km × 18 km.…”

Section: Construction Of Similar Habitatmentioning

confidence: 99%

A novel similar habitat potential model based on sliding‐window technique for vegetation restoration potential mapping

Zhang

Yao

et al. 2019

Land Degrad Dev

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Vegetation restoration potential mapping (VRPM) is of great importance for regional ecosystem restoration planning after long‐term land degradation or short‐term disasters. However, there are some problems to be solved in the current models for evaluating the potential of vegetation restoration. First, the models for VRPM are mostly established based on a knowledge‐driven index system. Although this kind of model is logically rigorous, it relies too much on expert knowledge and is relatively inefficient, especially for large‐area vegetation restoration assessments. Second, because of the spatial heterogeneity, as well as the absence of important indicators, the traditional global‐based evaluation models are difficult to adapt to the entire study area. In this study, an improved data‐driven approach, that is, a sliding‐window based similar habitat potential model, is developed for VRPM. The advantages of the new model include: (a) it is more efficient in determining the importance of each influencing factor without resorting to expert knowledge; (b) it is more locally adaptive than the global model because it performs sample training, rule building, and vegetation restoration potential calculation in each current local window. We provide a case‐study to show the modeling process and result interpretation of the new model.

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“…The selective search algorithm mainly uses a hierarchical clustering method to segment and get the candidate target areas. A more detailed description about the selective search algorithm is listed in references [23][24][25]. The candidate areas obtained using the selective search algorithm are sent to a convolution neural network for classification to determine their type.…”

Section: Target Segmentationmentioning

confidence: 99%

A Multi-Target Detection Algorithm for Infrared Image based on Retinex and LeNet5 Neural Network

Yun¹

2018

IJPE

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Object detection in infrared video images is an important and challenging work. Due to low resolution, poor contrast, and low visual quality, target detection in infrared images is inefficient and prone to having higher false positive and lower precision rates. To improve detection efficiency, according to the characteristics of infrared images, we proposed a multi-target detection algorithm based on image enhancement and the LeNet5 deep neural network. In our method, we used the Retinex image enhance algorithm to protrude the edge contour and contrast, highlight the detailed features, and enhance the overall visibility of infrared images. In particular, the LeNet5 convolution neural network and CVC vehicle-assisted driving database were used to train the interesting target in the infrared image to generate the target data model, and the selective search algorithm was used to segment the candidate detect object regions in the image. The separated candidate regions were sent to the trained data model to classify the type and locate the position of objects in the image. The simulation results in CVC infrared image subset datasets show that our algorithm has higher detection speed and accuracy than the traditional HOG-based and LBP-based detection algorithms.

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A batch sliding window method for local singularity mapping and its application for geochemical anomaly identification

Cited by 40 publications

References 33 publications

Satellite-Based Detection and Characterization of Industrial Heat Sources in China

Satellite-Based Detection and Characterization of Industrial Heat Sources in China

A novel similar habitat potential model based on sliding‐window technique for vegetation restoration potential mapping

A Multi-Target Detection Algorithm for Infrared Image based on Retinex and LeNet5 Neural Network

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