[1] Bin Wu, Bailang Yu, Shenjun Yao et al. A surface network based method for studying urban hierarchies by night time light remote sensing data. International Journal of Geographical Information Science, 33(2019).
[2] Hui Zhu, Qingling Zhang, Shan Zhang. Temporal and spatial characteristics of social and economic development in Central Asia based on night light remote sensing from 1992 to 2017. Journal of Earth Information Science, 22, 1449-1462(2020).
[3] X Cheng, H Shao, Y Li et al. Urban land intensive use evaluation study based on nighttime light—a case study of the yangtze river economic belt. Sustainability, 11, 675(2019).
[4] Lishuang Sun, Yaohui Han, Zhiwei Xie et al. Neighborhood extremum method for extracting urban built-up area using noctilucent remote sensing data. Journal of Wuhan University (Information Science Edition), 1-7.
[5] Xi Li, Xiangyu Xue. Estimation method of remote sensing power consumption based on Boston matrix. Journal of Wuhan University Information Science Edition, 43, 1994-2002(2018).
[6] Deren Li, Xi Li. On luminous remote sensing data mining. Acta Sinica Sinica, 44, 591-601(2015).
[7] Y Chang, S Wang, Y Zhou et al. A novel method of evaluating highway traffic prosperity based on nighttime light remote sensing. Remote Sensing, 12, 102(2019).
[8] W Jiang, G He, W Leng et al. Characterizing light pollution trends across protected areas in china using nighttime light remote sensing data. ISPRS International Journal of Geo-Information, 7, 243(2018).
[9] Kai Wen. Infrared dim small target detection and tracking algorithm based on complex fusion features and gray texture histogram descriptors. Science, technology and engineering, 16, 83-91(2016).
[10] B Yu, T Lian, Y Huang et al. Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement. International Journal of Geographical Information Science, 33, 687-706(2019).
[11] B Faouzi, P Washaya. Tracking Dynamic changes and monitoring socioeconomic parameters in algeria between 1993 and 2012,using nighttime light remote sensing. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2017, XLII-2/W7, 1127-1135.
[12] B Devkota, H Miyazaki, A Witayangkurn et al. Using volunteered geographic information and nighttime light remote sensing data to identify tourism areas of interest. Sustainability, 11, 4718(2019).
[13] R Swathi, A Srinivas. An improved image registration method using e-sift feature descriptor with hybrid optimization algorithm. Journal of the Indian Society of Remote Sensing, 48, 215-226(2020).
[14] Xing Wang, Dong Zhou, Wenmao Wang. Estimation of the building density of the main urban area of hefei using luojia no.1 luminous image. Remote sensing information, 35, 71-77(2020).
[15] Q Zhang, C Schaaf, K C Seto. The vegetation adjusted ntl urban index: a new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sensing of Environment, 129(2013).
[16] Guoliang Zhang, Ruifei Zhu, Yibo Du et al. Application of jilin-1 high resolution night light remote sensing image in urban monitoring. Satellite application, 27-33(2020).
[17] Y L S O Yongling Yao. House vacancy at urban areas in china with nocturnal light data of dmsp-ols(2011).
[18] Shuangchen Wu, Zhengrong Zuo. Infrared small target detection based on deep convolution neural network. J.Infrared Millim.Waves, 38, 371-380(2019).
[19] J Redmon, A Farhadi. Yolov3: an incremental improvement. arXiv e-prints, 1804-2767(2018).
[20] W Liu, D Anguelov, D Erhan et al. Ssd: single shot multibox detector(2016).
[21] Z Tian, C Shen, H Chen et al. Fcos: fully convolutional one-Stage object detection. arXiv e-prints, 1355-1904(2019).
[22] R Shaoqing, H Kaiming, G Ross et al. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(2017).
[23] K Simonyan, A Vedaldi, A Zisserman. Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv e-prints, 1312-6034(2013).
[24] J Dai, H Qi, Y Xiong et al. Deformable convolutional networks. arXiv e-prints, 1703-6211(2017).
[25] F Yu, V Koltun. Multi-scale context aggregation by dilated convolutions. arXiv e-prints, 1511-7122(2015).
[26] Y Cao, J Xu, S Lin et al. Gcnet: non-local networks meet squeeze-excitation networks and beyond. arXiv e-prints, 1904-11492(2019).
[27] R Changyong, P Duho, R Kyongchol et al. Semantic image description and classification based on generalized set. ICTACT Journal on Image and Video Processing, 8(2018).
[28] Deren Li, Guo Zhang, Xin Shen et al. Design and processing of noctilucent remote sensing of luojia-1 satellite. Acta remote sensing Sinica, 23, 1011-1022(2019).
[29] Ze Liang, Jiao Huang, Feili Wei et al. Spatial range identification of urban agglomerations in china based on noctilucent remote sensing images and baidu poi data. Geographic research, 39, 92-102(2020).
[30] F Yang, W Li, W Li et al. S3od: single stage small object detector from scratch for remote sensing images(2019).
