Automatic Aurora Image Classification Framework Based on Deep Learning for Occurrence Distribution Analysis: A Case Study of All‐Sky Image Data Sets From the Yellow River Station

The downstream plain of the Yellow River is experiencing some of the most severe groundwater depletion in China. Although the Chinese government has issued policies to ensure that the Yellow River can provide enough irrigation waters for this region, groundwater levels continue to decrease. Yucheng City was selected as a case study. A new method was designed to classify the cropland into various irrigated cropland. Subsequently, we analyzed data regarding these irrigated-cropland categories, irrigation norms, and the minimum amount of irrigation water being applied to cropland. The results showed that 91.5% of farmland can be classified as double irrigated (by both canal/river and well water), while 8.5% of farmland can be classified as well irrigated. During the irrigation season, the sediments brought in by the river have blocked portions of the canals. This has led to 23% of the double-irrigated cropland being irrigated by groundwater, and it is thus a main factor causing reductions in groundwater supply. These blocked canals should be dredged by local governments to mitigate local groundwater depletion. The method for classifying irrigated cropland from high-resolution images is valid and it can be used in other irrigated areas with a declining groundwater table for the sustainable use of groundwater resources.

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Distribution of phthalate esters in topsoil: a case study in the Yellow River Delta, China

Environmental Monitoring and Assessment ◽

10.1007/s10661-013-3190-7 ◽

2013 ◽

Vol 185 (10) ◽

pp. 8489-8500 ◽

Cited By ~ 43

Author(s):

Yang Hongjun ◽

Xie Wenjun ◽

Liu Qing ◽

Liu Jingtao ◽

Yu Hongwen ◽

...

Keyword(s):

Yellow River ◽

Phthalate Esters ◽

Yellow River Delta ◽

River Delta ◽

The Yellow River ◽

The Yellow River Delta

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Need to link river management with estuarine wetland conservation: A case study in the Yellow River Delta, China

Ocean & Coastal Management ◽

10.1016/j.ocecoaman.2017.06.004 ◽

2017 ◽

Vol 146 ◽

pp. 43-49 ◽

Cited By ~ 8

Author(s):

Rui Zhou ◽

Yunzhao Li ◽

Jingjing Wu ◽

Meng Gao ◽

Xiaoqing Wu ◽

...

Keyword(s):

Yellow River ◽

Yellow River Delta ◽

River Management ◽

Wetland Conservation ◽

River Delta ◽

The Yellow River ◽

The Yellow River Delta ◽

Estuarine Wetland

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An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

10.26686/wgtn.13158281.v1 ◽

2020 ◽

Author(s):

Ying Bi ◽

Bing Xue ◽

Mengjie Zhang

Keyword(s):

Feature Extraction ◽

Genetic Programming ◽

Image Classification ◽

Image Data ◽

Data Sets ◽

Automatic Feature Extraction ◽

Terminal Set ◽

Comparable Performance ◽

International Publishing ◽

High Level

© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.

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