{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T11:05:44Z","timestamp":1787655944326,"version":"build-2736575974"},"reference-count":69,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0126500"],"award-info":[{"award-number":["2019YFE0126500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["21-Y20B01-9001-19\/22"],"award-info":[{"award-number":["21-Y20B01-9001-19\/22"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["YJKYYQ20200010"],"award-info":[{"award-number":["YJKYYQ20200010"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science and Technology Major Project of China\u2019s High Resolution Earth Observation System","award":["2019YFE0126500"],"award-info":[{"award-number":["2019YFE0126500"]}]},{"name":"National Science and Technology Major Project of China\u2019s High Resolution Earth Observation System","award":["21-Y20B01-9001-19\/22"],"award-info":[{"award-number":["21-Y20B01-9001-19\/22"]}]},{"name":"National Science and Technology Major Project of China\u2019s High Resolution Earth Observation System","award":["YJKYYQ20200010"],"award-info":[{"award-number":["YJKYYQ20200010"]}]},{"name":"Scientific Instrument Developing Project of the Chinese Academy of Sciences","award":["2019YFE0126500"],"award-info":[{"award-number":["2019YFE0126500"]}]},{"name":"Scientific Instrument Developing Project of the Chinese Academy of Sciences","award":["21-Y20B01-9001-19\/22"],"award-info":[{"award-number":["21-Y20B01-9001-19\/22"]}]},{"name":"Scientific Instrument Developing Project of the Chinese Academy of Sciences","award":["YJKYYQ20200010"],"award-info":[{"award-number":["YJKYYQ20200010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multispectral remote sensing images have shown unique advantages in many fields, including military and civilian use. Facing the difficulty in processing cloud contaminated remote sensing images, this paper proposes a multispectral remote sensing image enhancement algorithm. A model is constructed from the aspects of cloud detection and image enhancement. In the cloud detection stage, clouds are divided into thick clouds and thin clouds according to the cloud transmitability in multi-spectral images, and a multi-layer cloud detection model is established. From the perspective of traditional image processing, a bimodal pre-detection algorithm is constructed to achieve thick cloud extraction. From the perspective of deep learning, the MobileNet algorithm structure is improved to achieve thin cloud extraction. Faced with the problem of insufficient training samples, a self-supervised network is constructed to achieve training, so as to meet the requirements of high precision and high efficiency cloud detection under the condition of small samples. In the image enhancement stage, the area where the ground objects are located is determined first. Then, from the perspective of compressed sensing, the signal is analyzed from the perspective of time and frequency domains. Specifically, the inter-frame information of hyperspectral images is analyzed to construct a sparse representation model based on the principle of compressed sensing. Finally, image enhancement is achieved. The experimental comparison between our algorithm and other algorithms shows that the average Area Overlap Measure (AOM) of the proposed algorithm reaches 0.83 and the Average Gradient (AG) of the proposed algorithm reaches 12.7, which is better than the other seven algorithms by average AG 2.<\/jats:p>","DOI":"10.3390\/rs14194815","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T03:30:37Z","timestamp":1664335837000},"page":"4815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet"],"prefix":"10.3390","volume":"14","author":[{"given":"Xuemei","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huping","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1007\/s11760-021-01885-7","article-title":"A review on deep learning techniques for cloud detection methodologies and challenges. Signal","volume":"15","author":"Li","year":"2021","journal-title":"Image Video Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Poli, G., Adembri, G., Gherardelli, M., and Tommasini, M. (2010, January 25\u201330). Dynamic threshold cloud detection algorithm improvement for AVHRR and SEVIRI images. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5653186"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.isprsjprs.2015.05.003","article-title":"Radiometric normalization and cloud detection of optical satellite images using invariant pixels","volume":"106","author":"Lin","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ba\u015feski, E., and Cenaras, \u00c7. (2015, January 16\u201319). Texture and color based cloud detection. Proceedings of the 2015 7th international conference on recent advances in space technologies (RAST), Istanbul, Turkey.","DOI":"10.1109\/RAST.2015.7208361"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1109\/JSTARS.2012.2191144","article-title":"MAP-MRF cloud detection based on PHD filtering","volume":"5","author":"Addesso","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Surya, S.R., and Simon, P. (2013, January 15\u201317). Automatic cloud detection using spectral rationing and fuzzy clustering. Proceedings of the 2013 2nd International Conference on Advanced Computing, Networking and Security, Mangalore, India.","DOI":"10.1109\/ADCONS.2013.44"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7264","DOI":"10.1109\/TGRS.2014.2310240","article-title":"Cloud Detection of RGB Color Aerial Photographs by Progressive Refinement Scheme","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zi, Y., Xie, F., and Jiang, Z. (2018). A Cloud Detection Method for Landsat 8 Images Based on PCANet. Remote Sens., 10.","DOI":"10.3390\/rs10060877"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/JSTARS.2018.2800701","article-title":"Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial\u2013Spectral Total Variation","volume":"11","author":"He","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","first-page":"12","article-title":"Spatial cloud detection and retrieval system for satellite images","volume":"3","author":"Onsi","year":"2012","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_11","first-page":"W1","article-title":"Cloud detection method based on feature extraction in remote sensing images","volume":"2","author":"Changhui","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1109\/JSTARS.2017.2686488","article-title":"Multilevel cloud detection in remote sensing images based on deep learning","volume":"10","author":"Xie","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shao, Z., Deng, J., Wang, L., Fan, Y., Sumari, N.S., and Cheng, Q. (2017). Fuzzy autoencode based cloud detection for remote sensing imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040311"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5366","DOI":"10.1109\/TGRS.2017.2706326","article-title":"Denoising of hyperspectral images using nonconvex low rank matrix approximation","volume":"55","author":"Chen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, X., Qin, F., and Qin, Y. (2010, January 29\u201331). Study on the thick cloud removal method based on multi-temporal remote sensing images. Proceedings of the 2010 International Conference on Multimedia Technology, Ningbo, China.","DOI":"10.1109\/ICMULT.2010.5631017"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1080\/01431161003621619","article-title":"An optimal image transform for threshold-based cloud detection using heteroscedastic discriminant analysis","volume":"32","author":"Marais","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qian, J., Luo, Y., Wang, Y., and Li, D. (2016, January 10\u201315). Cloud detection of optical remote sensing image time series using mean shift algorithm. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729139"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","article-title":"Hyperspectral and multispectral image fusion based on a sparse representation","volume":"53","author":"Wei","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/JSEE.2014.00124","article-title":"Cloud removal of remote sensing image based on multi-output support vector regression","volume":"25","author":"Hu","year":"2014","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.1080\/01431161.2019.1580788","article-title":"Energy-based cloud detection in multispectral images based on the SVM technique","volume":"40","author":"Sui","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1016\/j.patcog.2012.10.030","article-title":"Unsupervised methods for the classification of hyperspectral images with low spatial resolution","volume":"46","author":"Villa","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ozkan, S., Efendioglu, M., and Demirpolat, C. (2018, January 22\u201327). Cloud detection from RGB color remote sensing images with deep pyramid networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519570"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, H., Zeng, D., and Tian, Q. (2018, January 13\u201316). Super-pixel cloud detection using hierarchical fusion CNN. Proceedings of the2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), Xi\u2019an, China.","DOI":"10.1109\/BigMM.2018.8499091"},{"key":"ref_25","first-page":"29","article-title":"DABNet: Deformable contextual and boundary-weighted network for cloud detection in remote sensing images","volume":"60","author":"He","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral image denoising employing a spatial\u2013spectral deep residual convolutional neural network","volume":"57","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bai, T., Li, D., Sun, K., Chen, Y., and Li, W. (2016). Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion. Remote Sens., 8.","DOI":"10.3390\/rs8090715"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s11082-020-02500-8","article-title":"Lightweight U-Net for cloud detection of visible and thermal infrared remote sensing images","volume":"52","author":"Zhang","year":"2020","journal-title":"Opt. Quantum Electron."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s41651-019-0037-y","article-title":"Cloud detection in high-resolution remote sensing images using multi-features of ground objects","volume":"3","author":"Zhang","year":"2019","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_30","first-page":"1","article-title":"CNN cloud detection algorithm based on channel and spatial attention and probabilistic upsampling for remote sensing image","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1109\/TNNLS.2020.2978756","article-title":"3-D quasi-recurrent neural network for hyperspectral image denoising","volume":"32","author":"Wei","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hwang, S.J., Kapoor, A., and Kang, S.B. (2012). Context-based automatic local image enhancement. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-33718-5_41"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.optlastec.2011.07.009","article-title":"Image enhancement using multi scale image features extracted by top-hat transform","volume":"44","author":"Bai","year":"2012","journal-title":"Opt. Laser Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"114512","DOI":"10.1016\/j.eswa.2020.114512","article-title":"A novel method for multispectral image pansharpening based on high dimensional model representation","volume":"170","author":"Tunga","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Choi, Y., Kim, N., Hwang, S., and Kweon, I.S. (2016, January 9\u201314). Thermal image enhancement using convolutional neural network. Proceedings of the 2016 IEEE\/RSJ international conference on intelligent robots and systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759059"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104171","DOI":"10.1016\/j.engappai.2021.104171","article-title":"Bayesian retinex underwater image enhancement","volume":"101","author":"Zhuang","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s00259-021-05478-x","article-title":"Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise","volume":"49","author":"Mehranian","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_38","first-page":"1","article-title":"Image enhancement using fuzzy technique","volume":"2","author":"Mahashwari","year":"2013","journal-title":"Int. J. Res. Eng. Sci. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.patrec.2013.08.024","article-title":"Image enhancement using exposure based sub image histogram equalization","volume":"36","author":"Singh","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.aeue.2013.08.015","article-title":"A fast and efficient color image enhancement method based on fuzzy-logic and histogram","volume":"68","author":"Raju","year":"2014","journal-title":"AEU-Int. J. Electron. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5025","DOI":"10.1080\/01431161.2021.1906981","article-title":"Water-Body segmentation for multi-spectral remote sensing images by feature pyramid enhancement and pixel pair matching","volume":"42","author":"Chen","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1109\/TGRS.2012.2187063","article-title":"Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis","volume":"50","author":"Liu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pathak, S.S., Dahiwale, P., and Padole, G. (2015, January 20). A combined effect of local and global method for contrast image enhancement. Proceedings of the 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India.","DOI":"10.1109\/ICETECH.2015.7275011"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5581","DOI":"10.1109\/TIP.2015.2474697","article-title":"The stone transform: Multi-resolution image enhancement and compressive video","volume":"24","author":"Goldstein","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.ins.2020.09.066","article-title":"A novel multi-scale fusion framework for detail-preserving low-light image enhancement","volume":"548","author":"Xu","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Stankevich, S.A., Piestova, I.O., Lubskyi, M.S., Shklyar, S.V., Lysenko, A.R., Maslenko, O.V., and Rabcan, J. (2021). Knowledge-based multispectral remote sensing imagery superresolution. Reliability Engineering and Computational Intelligence, Springer.","DOI":"10.1007\/978-3-030-74556-1_13"},{"key":"ref_47","first-page":"1","article-title":"An adaptive gamma correction for image enhancement","volume":"1","author":"Rahman","year":"2016","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/TIP.2018.2810539","article-title":"Structure-revealing low-light image enhancement via robust retinex model","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neucom.2018.11.081","article-title":"Single infrared image enhancement using a deep convolutional neural network","volume":"332","author":"Kuang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Usharani, A., and Bhavana, D. (2021). Deep convolution neural network based approach for multispectral images. Int. J. Syst. Assur. Eng. Manag., 1\u201310.","DOI":"10.1007\/s13198-021-01133-8"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/TGRS.2019.2946050","article-title":"Nonlocal tensor-ring decomposition for hyperspectral image denoising","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","unstructured":"Wen, H., Tian, Y., Huang, T., and Gao, W. (2013, January 19\u201323). Single underwater image enhancement with a new optical model. Proceedings of the 2013 IEEE International Symposium on Circuits and Systems (ISCAS), Beijing, China."},{"key":"ref_53","unstructured":"Shen, L., Yue, Z., Feng, F., Chen, Q., Liu, S., and Ma, J. (2017). Msr-net: Low-light image enhancement using deep convolutional network. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.ijleo.2017.10.132","article-title":"Remote sensing image enhancement using hazy image model","volume":"155","author":"Kaplan","year":"2018","journal-title":"Optik"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"10321","DOI":"10.1364\/OE.413164","article-title":"Underwater image enhancement method based on adaptive attenuation-curve prior","volume":"29","author":"Liu","year":"2021","journal-title":"Opt. Express"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/TBME.2017.2700627","article-title":"Color retinal image enhancement based on luminosity and contrast adjustment","volume":"65","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kim, H.U., Koh, Y.J., and Kim, C.S. (2020). PieNet: Personalized image enhancement network. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58577-8_23"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Moran, S., Marza, P., McDonagh, S., Parisot, S., and Slabaugh, G. (2020, January 13\u201319). Deeplpf: Deep local parametric filters for image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01284"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, H., Xie, Q., Yao, L., and Chen, Q. (2018). Research on a surface defect detection algorithm based on MobileNet-SSD. Appl. Sci., 8.","DOI":"10.3390\/app8091678"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"162499","DOI":"10.1109\/ACCESS.2021.3130342","article-title":"Detection and localization of multiple image splicing using MobileNet V1","volume":"9","author":"Kadam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Srinivasu, P.N., SivaSai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., and Kang, J.J. (2021). Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 21.","DOI":"10.3390\/s21082852"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kavyashree, P.S., and El-Sharkawy, M. (2021, January 27\u201330). Compressed mobilenet v3: A light weight variant for resource-constrained platforms. Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Online.","DOI":"10.1109\/CCWC51732.2021.9376113"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"28217","DOI":"10.1007\/s11042-021-10860-w","article-title":"Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm","volume":"80","author":"Rahebi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1007\/s00500-019-04011-5","article-title":"Fast curvelet transform through genetic algorithm for multimodal medical image fusion","volume":"24","author":"Arif","year":"2020","journal-title":"Soft Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.infrared.2018.11.025","article-title":"The infrared moving target extraction and fast video reconstruction algorithm","volume":"97","author":"Qiu","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s42979-020-0109-6","article-title":"Detection of ovarian cyst in ultrasound images using fine-tuned VGG-16 deep learning network","volume":"1","author":"Srivastava","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.procs.2019.11.147","article-title":"Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3","volume":"161","author":"Sam","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.patrec.2020.03.007","article-title":"The classification of gliomas based on a pyramid dilated convolution resnet model","volume":"133","author":"Lu","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s12046-017-0673-1","article-title":"Visible and NIR image fusion using weight-map-guided Laplacian\u2013Gaussian pyramid for improving scene visibility","volume":"42","author":"Vanmali","year":"2017","journal-title":"S\u0101dhan\u0101"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4815\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:40:15Z","timestamp":1760143215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,27]]},"references-count":69,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194815"],"URL":"https:\/\/doi.org\/10.3390\/rs14194815","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,27]]}}}