{"title":"用于光谱非混合的多流金字塔协作网络","authors":"Jie Wang, Mengying Ni, Zhixiang Wang, Yu Yan, Xiang Cheng, Jindong Xu","doi":"10.1080/01431161.2024.2334772","DOIUrl":null,"url":null,"abstract":"Convolutional autoencoder, which can well model the spatial correlation of the data, have been widely applied to spectral unmixing task and achieved desirable performance. However, the fixed geomet...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"66 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-stream pyramid collaborative network for spectral unmixing\",\"authors\":\"Jie Wang, Mengying Ni, Zhixiang Wang, Yu Yan, Xiang Cheng, Jindong Xu\",\"doi\":\"10.1080/01431161.2024.2334772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Convolutional autoencoder, which can well model the spatial correlation of the data, have been widely applied to spectral unmixing task and achieved desirable performance. However, the fixed geomet...\",\"PeriodicalId\":14369,\"journal\":{\"name\":\"International Journal of Remote Sensing\",\"volume\":\"66 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/01431161.2024.2334772\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/01431161.2024.2334772","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
Multi-stream pyramid collaborative network for spectral unmixing
Convolutional autoencoder, which can well model the spatial correlation of the data, have been widely applied to spectral unmixing task and achieved desirable performance. However, the fixed geomet...
期刊介绍：
The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include:
• Remotely sensed data collection, analysis, interpretation and display.
• Surveying from space, air, water and ground platforms.
• Imaging and related sensors.
• Image processing.
• Use of remotely sensed data.
• Economic surveys and cost-benefit analyses.
• Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
