论文地址:Deep learning in multimodal remote sensing data fusion: A comprehensive review 摘要 随着遥感(RS)技术的飞速发展,大量具有相当复杂异质性的地球观测(EO)数据随处可见,这为研究人员提供了以全新方式解决当前地球科学应用的机会。 近年来,随着地球观测数据的联合利用,多模态遥感数据融合研究取得了巨大进展,但...
Classification and identification of the materials lying over or beneath the earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS), and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although ...
A python tool to perform deep learning experiments on multimodal remote sensing data. - zhe-meng/Multimodal-Remote-Sensing-Toolkit
the multimodal deep learning (MDL) models were developed and rigorously validated using atmospherically corrected Landsat remote sensing reflectance data and synchronous water quality measurements for estimating long-term Chlorophyll-a (Chl-a), total phosphorus (TP), and total nitrogen (TN) in Lake Sim...
FBNet -> Feature Balance for Fine-Grained Object Classification in Aerial Images SuperYOLO -> SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing ImagerySalient object detectionDetecting the most noticeable or important object in a sceneACCo...
- 《IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing》 被引量: 28发表: 2018年 Deep Multimodal Representation Learning from Temporal Data In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint ...
“A 13.6 percent improvement in time to detect initial smoke was reported, and F1 mean improved by 1.10 while standard deviation decreased by 0.32 on average for smoke detection performance, allowing us to conclude that the Multimodal SmokeyNet model was the most efficient and stable of the ...
Initially, we made a Web of Science Core Collection search withapp the following keyword combination in the “all fields” category: {“remote sensing” AND (“deep learning” OR “convolutional neural” OR “recurrent neural”) AND (“small data” OR “small sample” OR “limited sample” ...
“A 13.6 percent improvement in time to detect initial smoke was reported, and F1 mean improved by 1.10 while standard deviation decreased by 0.32 on average for smoke detection performance, allowing us to conclude that the Multimodal SmokeyNet model was the most efficient and stable of the ...
Similarly, street-level images taken at one location are additional input to one pixel in a satellite raster essentially appending a layer of visual information. Building on these principles, we propose two multimodal deep learning approaches: SATinSL (augmented street-level network) and SLinSAT (...