Small UAV-based multi-temporal change detection for monitoring cultivated land cover changes in mountainous terrain

Song, Fei; Dan, Tingting; Yu, Rui; Yang, Kun; Yang, Yang; Chen, Weiyang; Gao, Xueyan; Ong, Sim‐Heng

doi:10.1080/2150704x.2019.1576949

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…These have become the core goals of sustainable utilization of cultivated land in the Southwest mountainous areas (Shao, Zhang, & Li, 2016). Therefore, it is necessary to use remote sensing technology to monitor cultivated land cover and its dynamic change at different time scales (Song et al, 2019) and take adequate protection measures to maintain national food security better. The purpose of this study is to: (a) clarify the evolution characteristics of cultivated land in Southwest China, (b) explore the driving mechanism of cultivated land evolution in mountainous areas, and (c) propose improvement measures for cultivated land protection in Southwest China.…”

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confidence: 99%

Exploring cultivated land evolution in mountainous areas of Southwest China, an empirical study of developments since the 1980s

et al. 2020

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Cultivated land protection is related to food security and sustainable development. As an important reserve resource, the protection and development of cultivated land in mountainous areas need attention from governments and academia. This paper analyses the long‐term evolution process cultivated land in mountainous areas of Southwest China by using large‐scale remote sensing, topographic relief amplitude model, and a modified binary logistic regression model, to understand internal reasons for evolution differentiation at the macro level. The results show that untransformed cultivated land is mainly distributed below the topographic relief amplitude of 770 m. The transformation direction and spatial characteristics of transformed cultivated land gradually shift to low relief amplitude (<631 m) areas with time. Construction land accounts for 51.11% of the cultivated land transformation, usually distributed in flatlands or basins below the relief amplitude of 498 m, and hilly cultivated land around it displays a fluctuating recovery process. Topography is the most important factor affecting the evolution of cultivated land in mountainous areas. However, there are still differences in the driving mechanism of cultivated land evolution types, and some key policies in China in the past directly affected the evolution paths of cultivated land in mountainous areas. The research results can provide ideas for the utilization and protection of cultivated land in similar mountainous areas.

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confidence: 99%

Exploring cultivated land evolution in mountainous areas of Southwest China, an empirical study of developments since the 1980s

et al. 2020

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“…σ 2 is the covariance parameter. This is a method for obtaining a more reliable correspondence estimation, which uses the dynamic outlier selection to gradually identify outliers and maximize the number of reliable inlier pairs, thus helping establish a coarse to fine transformation. In the M-step, estimate the optimal parameter values Ψ by minimizing the expectation of the complete negative log-likelihood function given below. …”

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confidence: 99%

Automatic Detection of Scattered Garbage Regions Using Small Unmanned Aerial Vehicle Low-Altitude Remote Sensing Images for High-Altitude Natural Reserve Environmental Protection

Chen

Zhao

You

et al. 2021

Environ. Sci. Technol.

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Recently, some famous high-altitude nature reserves have been shut down due to tourist garbage pollution. In order to clean up such garbage more conveniently and quickly, a novel detection framework is proposed to automatically detect scattered garbage regions using low-altitude remote sensing of small unmanned aerial vehicles (SUAVs), and it contains the following steps. First, high-resolution, low-altitude, multitemporal remote sensing images containing scattered garbage regions are collected by SUAVs, and two data augmentation methods are proposed to expand the training samples. Second, low-altitude remote sensing image registration and target-level image change detection are used to extract the candidate regions of garbage. Finally, a deep learning detection network is adopted to classify the scattered garbage regions. Experimental results show that the proposed detection framework achieves a mean accuracy of 96.94% and provides better performances on the real dataset compared with state-of-the-art methods.

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“…Liu et al proposed an effective end-to-end visual saliency model for K-means clustering which extracts both dense and multiscale features through dense spatial pyramidal pooling (DSPP), which helps to extract buildings at all scales [14]. Song et al proposed a visual saliency model that uses separable factorization residual blocks as well as inflated convolution, aiming to guarantee a small accuracy loss with low computational cost and memory consumption [15]. Although highresolution remote sensing images provide rich feature information, they also bring about a large amount of significant noise.…”

Section: Related Workmentioning

confidence: 99%

Construction of a Visual Saliency Model for Neighborhood Building Landmarks Based on K‐Means Clustering

Qiao

2021

Advances in Civil Engineering

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In this paper, firstly, based on the quantitative relationship between K-means clustering and visual saliency of neighborhood building landmarks, the weights occupied by each index of composite visual factors are obtained by using multiple statistical regression methods, and, finally, we try to construct a saliency model of multiple visual index composites and analyze and test the model. As regards decomposition and quantification of visual saliency influencing factors, to describe and quantify these visual significance factors of the landmarks, the significant factors are decomposed into several quantifiable secondary indicators. Considering that the visual saliency of the landmarks in the neighborhood is reflected by the variance of the influencing factors and that the scope of the landmarks is localized, the local outlier detection algorithm is used to solve the variance of the secondary indicators. Since the visual significance of neighborhood building landmarks is influenced by a combination of influencing factors, the overall difference degree of secondary indicators is calculated by K-means clustering. To facilitate the factor calculation, a factor-controlled virtual environment was built to carry out the experimental study of landmark perception and calculate the different degrees of each index of the building. The data of visual indicators of the neighborhood buildings for this experiment were also collected, and the significance values of the neighborhood buildings were calculated. The influence weights of the indicators were obtained by using multiple linear regression analysis, the visual significance model of the landmarks of the neighborhood buildings in the factor-controlled environment was constructed, and the model was analyzed and tested.

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Small UAV-based multi-temporal change detection for monitoring cultivated land cover changes in mountainous terrain

Cited by 15 publications

References 13 publications

Exploring cultivated land evolution in mountainous areas of Southwest China, an empirical study of developments since the 1980s

Exploring cultivated land evolution in mountainous areas of Southwest China, an empirical study of developments since the 1980s

Automatic Detection of Scattered Garbage Regions Using Small Unmanned Aerial Vehicle Low-Altitude Remote Sensing Images for High-Altitude Natural Reserve Environmental Protection

Construction of a Visual Saliency Model for Neighborhood Building Landmarks Based on K‐Means Clustering

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