Zobrazeno 1 - 10
of 646
pro vyhledávání: '"SITS"'
Autor:
Hengbin Wang, Zijing Ye, Yu Yao, Wanqiu Chang, Junyi Liu, Yuanyuan Zhao, Shaoming Li, Zhe Liu, Xiaodong Zhang
Publikováno v:
Geo-spatial Information Science, Pp 1-16 (2024)
Cross-Regional Model Transfer (CRMT) provides a solution to crop classification challenges in target regions with limited labeled samples. However, when the source region (source domain) and the target region (target domain) are spatially distant, a
Externí odkaz:
https://doaj.org/article/d8c45e80026f46c1ad70e1c64406d917
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1681-1694 (2025)
Given the systematic acquisition of satellite data, it is possible to generate up-to-date land cover (LC) maps, essential for effective agricultural territory management, environmental monitoring, and informed decision-making. Typically, creating a L
Externí odkaz:
https://doaj.org/article/90fe3160da114cd680628cb1ab43f373
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1272-1287 (2025)
With the advancement of remote sensing satellite technology, the acquisition of Satellite Image Time-Series (SITS) data has significantly increased, providing new opportunities and challenges for land cover analysis. Traditional unsupervised clusteri
Externí odkaz:
https://doaj.org/article/dd1ef004b4784e5ba840a2836379c6c9
Autor:
Gregory Giuliani
Publikováno v:
Big Earth Data, Vol 8, Iss 3, Pp 435-466 (2024)
Accurate, consistent, and high-resolution Land Use & Cover (LUC) information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers, pressures, state, and impacts on land systems. Nevertheless, the availability o
Externí odkaz:
https://doaj.org/article/d4de6d51170a40eb8064cb667f290acc
Autor:
Emmanuel Tetteh Jumpah, Abdulai Adams, Tomas Ratinger, Bernard Kwamena Cobinna Essel, Forzia Ibrahim
Publikováno v:
Cogent Food & Agriculture, Vol 10, Iss 1 (2024)
Adopting sustainable intensification technologies improves the productivity, incomes, and livelihoods of small farm households; however, their adoption remains disproportionately low. Although soil, climate and vegetative cover are important factors
Externí odkaz:
https://doaj.org/article/805844b1336645db93f98432129654c7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11685-11706 (2024)
While satellite time series are essential tools to derive phenometrics at unprecedented spatial and temporal scales, nonsystematic acquisition or medium spatial resolution of available missions is potentially problematic. At the same time, low-cost o
Externí odkaz:
https://doaj.org/article/7287c08b15984830a942ff1a17419f75
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4350-4367 (2024)
In this article, a new self-supervised strategy for learning meaningful representations of complex optical satellite image time series (SITS) is presented. The methodology proposed, named Unet-BERT spAtio-temporal Representation eNcoder (U-BARN), exp
Externí odkaz:
https://doaj.org/article/78300210f8244fe1be47ded252e25742
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2980-2994 (2024)
In this article, we propose a method exploiting irregular and unaligned Sentinel-2 satellite image time series (SITS) for large-scale land cover pixel-based classification. We perform end-to-end learning by combining a time and space informed kernel
Externí odkaz:
https://doaj.org/article/c1c176894458402eb8b83a3da2500f5f
Publikováno v:
Remote Sensing, Vol 16, Iss 22, p 4225 (2024)
Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, t
Externí odkaz:
https://doaj.org/article/723673c34a1f4bdfb431db3d4a8b0007
Autor:
Jayanth Shenoy, Xingjian Davis Zhang, Bill Tao, Shlok Mehrotra, Rem Yang, Han Zhao, Deepak Vasisht
Publikováno v:
Remote Sensing, Vol 16, Iss 18, p 3470 (2024)
Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task
Externí odkaz:
https://doaj.org/article/d44c979566314e8ca6c50b6b58ae2679