{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T03:33:34Z","timestamp":1776310414475,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T00:00:00Z","timestamp":1618358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nowadays, as the number of remote sensing satellites launched and applied in China has been mounting, relevant institutions\u2019 workload of processing raw satellite images to be distributed to users is also growing. However, due to factors such as extreme atmospheric conditions, diversification of on-board device status, data loss during transmission and algorithm issues of ground systems, defect of image quality is inevitable, including abnormal color, color cast, data missing, obvious stitching line between Charge-Coupled Devices (CCDs), and inconstant radiation values between CCDs. Product application has also been impeded. This study presents a unified framework based on well-designed features an Artificial Neural Network (ANN) to automatically identify defective images. Samples were collected to form the dataset for training and validation, systematic experiments designed to verify the effectiveness of the features, and the optimal network architecture of ANN determined. Moreover, an effective method was proposed to explain the inference of ANN based on local gradient approximation. The recall of our final model reached 81.18% and F1 score 80.13%, verifying the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/rs13081506","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T04:21:08Z","timestamp":1618374068000},"page":"1506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Unified Framework for Anomaly Detection of Satellite Images Based on Well-Designed Features and an Artificial Neural Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Haibo","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China"},{"name":"China Centre for Resources Satellite Data and Application (CRESDA), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyong","family":"Yu","sequence":"additional","affiliation":[{"name":"China Centre for Resources Satellite Data and Application (CRESDA), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangbin","family":"You","sequence":"additional","affiliation":[{"name":"China Centre for Resources Satellite Data and Application (CRESDA), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruolin","family":"Ma","sequence":"additional","affiliation":[{"name":"China Centre for Resources Satellite Data and Application (CRESDA), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weilin","family":"Wang","sequence":"additional","affiliation":[{"name":"China Centre for Resources Satellite Data and Application (CRESDA), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,14]]},"reference":[{"key":"ref_1","first-page":"774","article-title":"Current Status and Future Prospects of Remote Sensing","volume":"32","author":"Zhang","year":"2017","journal-title":"Bull. Chin. Acad. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., and Zhang, C. (2020). A Review of Remote Sensing for Environmental Monitoring in China. Remote Sens., 12.","DOI":"10.3390\/rs12071130"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.patcog.2003.12.007","article-title":"Color balancing of digital photos using simple image statistics","volume":"37","author":"Gasparini","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine Learning: Trends, Perspectives, and Prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mitchell, T.M. (1997). Introduction. Machine Learning, McGraw-Hill. [1st ed.].","DOI":"10.1007\/978-1-4899-4587-7_1"},{"key":"ref_6","unstructured":"Christopher, M.B. (2006). Introduction. Machine Learning and Pattern Recognition, Springer. [1st ed.]."},{"key":"ref_7","unstructured":"Ian, G., Yoshua, B., and Aaron, C. (2016). Machine Learning Basics. Deep Learning, MIT Press. [1st ed.]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_9","unstructured":"Nair, V., and Hinton, G. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel."},{"key":"ref_10","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (May, January 30). Searching for Activation Functions. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kaur, A., and Wasson, V. (2017, January 19\u201320). A Novel Approach to no-Reference Image Quality Assessment using Canny Magnitude Based upon Neural Network. Proceedings of the 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT.2017.8256904"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"211301","DOI":"10.1007\/s11432-019-2757-1","article-title":"Perceptual image quality assessment: A survey","volume":"63","author":"Zhai","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alaql, O., Ghazinour, K., and Chang, C. (2016, January 11\u201313). Classification of Image Distortions Based on Features Evaluation. Proceedings of the 18th IEEE International Symposium on Multimedia (IEEE ISM), San Jose, CA, USA.","DOI":"10.1109\/ISM.2016.0076"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Z., Athar, S., and Wang, Z. (2019, January 24\u201326). Blind Quality Assessment of Multiply Distorted Images Using Deep Neural Networks. Proceedings of the International Conference on Image Analysis and Recognition (ICIAR), Waterloo, ON, Canada.","DOI":"10.1007\/978-3-030-27202-9_8"},{"key":"ref_17","unstructured":"Sebastian, B., Dominique, M., Thomas, W., and Wojciech, S. (2016, January 25\u201328). A deep neural network for image quality assessment. Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xia, Y., and Chen, Z. (2015, January 9\u201312). Quality Assessment for Remote Sensing Images: Approaches and Applications. Proceedings of the IEEE International Conference on Systems Man and Cybernetics Conference, City University of Hong Kong, Hong Kong, China.","DOI":"10.1109\/SMC.2015.186"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Agudelo-Medina, O.A., Benitez-Restrepo, H.D., Vivone, G., and Bovik, A. (2019). Perceptual quality assessment of pan-sharpened images. Remote Sens., 11.","DOI":"10.3390\/rs11070877"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jingxiang, Y., Yongqiang, Z., Chen, Y., and Cheung-Wai, C.J. (2017). No-reference hyperspectral image quality assessment via quality-sensitive features learning. Remote Sens., 9.","DOI":"10.3390\/rs9040305"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6539","DOI":"10.3390\/rs5126539","article-title":"Spatial Quality Assessment of Pan-Sharpened High Resolution Satellite Imagery Based on an Automatically Estimated Edge Based Metric","volume":"5","author":"Javan","year":"2013","journal-title":"Remote Sens."},{"key":"ref_22","unstructured":"Alice, Z., and Amanda, C. (2018). Introduction. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, O\u2019Reilly Media. [1st ed.]."},{"key":"ref_23","unstructured":"Hunt, R. (1998). Measuring Color, Fountain Press. [3rd ed.]."},{"key":"ref_24","unstructured":"Zhi, J. (2014). The Research of Color Cast Detection in Lab Color Space. [Master\u2019s Thesis, Xidian University]."},{"key":"ref_25","unstructured":"Weihua, X., Brain, F., and Lilong, S. (2007, January 5\u20139). Automatic White Balancing via Grey Surface Identification. Proceedings of the 15th Color Imaging Conference, Albuquerque, NM, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hasler, D., and S\u00fcsstrunk, S. (2003). Measuring Colorfulness in Natural Images. Human Vision and Electronic Imaging VIII, SPIE.","DOI":"10.1117\/12.477378"},{"key":"ref_27","unstructured":"Zhou, Z.H. (2016). Neural Networks. Machine Learning, Tsinghua University Press. [1st ed.]."},{"key":"ref_28","unstructured":"Marco, T.R., Sameer, S., and Carlos, G. (2016, January 13\u201317). Why Should I Trust You?: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","article-title":"Explaining nonlinear classification decisions with deep Taylor decomposition","volume":"65","author":"Montavon","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_32","unstructured":"Baker, B., Gupta, O., Naik, N., and Raskar, R. (2017, January 24\u201326). Designing Neural Network Architectures using Reinforcement Learning. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_33","unstructured":"Bello, I., Zoph, B., Vasudevan, V., and Le, Q. (2017, January 24\u201326). Neural Optimizer Search with Reinforcement Learning. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:47:47Z","timestamp":1760161667000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,14]]},"references-count":34,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081506"],"URL":"https:\/\/doi.org\/10.3390\/rs13081506","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,14]]}}}