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pro vyhledávání: '"Albanwan, Hessah"'
A critical step in the digital surface models(DSM) generation is feature matching. Off-track (or multi-date) satellite stereo images, in particular, can challenge the performance of feature matching due to spectral distortions between images, long ba
Externí odkaz:
http://arxiv.org/abs/2409.02825
Autor:
Albanwan, Hessah
Remote sensing (RS) images are important to monitor and survey earth at varying spatial scales. Continuous observations from various RS sources complement single observations to improve applications. Fusion into single or multiple images provides mor
Externí odkaz:
http://arxiv.org/abs/2404.17851
Autor:
Albanwan, Hessah
The analysis of time-sequence satellite images is a powerful tool in remote sensing; it is used to explore the statics and dynamics of the surface of the earth. Usually, the quality of multitemporal images is influenced by metrological conditions, hi
Externí odkaz:
http://arxiv.org/abs/2404.18950
Autor:
Albanwan, Hessah
Remote sensing (RS) images play an important role in monitoring and surveying the earth’s surface at varying spatial scales. Continuous observations from various remote sensing sources over time complement single observations and provide better int
Autor:
Albanwan, Hessah, Qin, Rongjun
Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robus
Externí odkaz:
http://arxiv.org/abs/2210.14031
The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades. MMSs have been widely used to provide valuable assets in different applications. This has been facilitated by the wide availability of low-cost sensors,
Externí odkaz:
http://arxiv.org/abs/2205.15865
Autor:
Albanwan, Hessah, Qin, Rongjun
Deep learning (DL) methods are widely investigated for stereo image matching tasks due to their reported high accuracies. However, their transferability/generalization capabilities are limited by the instances seen in the training data. With satellit
Externí odkaz:
http://arxiv.org/abs/2205.14051
Autor:
Albanwan, Hessah, Qin, Rongjun
Remote sensing images and techniques are powerful tools to investigate earth surface. Data quality is the key to enhance remote sensing applications and obtaining a clear and noise-free set of data is very difficult in most situations due to the vary
Externí odkaz:
http://arxiv.org/abs/2107.02701
The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multitemporal data set. Due to varying acquisition conditions (e.g., illumination, sensors, seasonal differences), the classificatio
Externí odkaz:
http://arxiv.org/abs/2107.00590
Autor:
Albanwan, Hessah AMYM
The analysis of time-sequence satellite images is a powerful tool in remote sensing; it is used to explore the statics and dynamics of the surface of the earth. Usually, the quality of multitemporal images is influenced by metrological conditions, hi