The Experts below are selected from a list of 4125 Experts worldwide ranked by ideXlab platform

Interdonato Roberto - One of the best experts on this subject based on the ideXlab platform.

  • Weakly Supervised learning for land cover mapping of satellite image time series via attention-based CNN
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Ienco Dino, Gaetano Raffaele, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto
    Abstract:

    International audienceThe unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at Coarse Granularity level carrying out inexact and weak knowledge that can affect the whole process.To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the Coarse Granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions

  • Weakly supervised learning for landcover mapping of satellite image timeseries via attention-based CNN
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Ienco Dino, Gaetano Raffaele, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto
    Abstract:

    The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at Coarse Granularity level carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the Coarse Granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions

  • Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation
    2020
    Co-Authors: Ienco Dino, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto, Gaetano Raffaele
    Abstract:

    Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data (series of images with high revisit time period on the same geographical area) is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich and complex image data. One of the main task associated to SITS data analysis is related to land cover mapping where satellite data are exploited via learning methods to recover the Earth Surface status aka the corresponding land cover classes. Due to operational constraints, the collected label information, on which machine learning strategies are trained, is often limited in volume and obtained at Coarse Granularity carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), that is able to intelligently exploit the weak supervision provided by the Coarse Granularity labels. Furthermore, our framework also produces an additional side-information that supports the model interpretability with the aim to make the black box gray. Such side-information allows to associate spatial interpretation to the model decision via visual inspection.Comment: Under submission to Elsevier journa

Ienco Dino - One of the best experts on this subject based on the ideXlab platform.

  • Weakly Supervised learning for land cover mapping of satellite image time series via attention-based CNN
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Ienco Dino, Gaetano Raffaele, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto
    Abstract:

    International audienceThe unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at Coarse Granularity level carrying out inexact and weak knowledge that can affect the whole process.To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the Coarse Granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions

  • Weakly supervised learning for landcover mapping of satellite image timeseries via attention-based CNN
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Ienco Dino, Gaetano Raffaele, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto
    Abstract:

    The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at Coarse Granularity level carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the Coarse Granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions

  • Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation
    2020
    Co-Authors: Ienco Dino, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto, Gaetano Raffaele
    Abstract:

    Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data (series of images with high revisit time period on the same geographical area) is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich and complex image data. One of the main task associated to SITS data analysis is related to land cover mapping where satellite data are exploited via learning methods to recover the Earth Surface status aka the corresponding land cover classes. Due to operational constraints, the collected label information, on which machine learning strategies are trained, is often limited in volume and obtained at Coarse Granularity carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), that is able to intelligently exploit the weak supervision provided by the Coarse Granularity labels. Furthermore, our framework also produces an additional side-information that supports the model interpretability with the aim to make the black box gray. Such side-information allows to associate spatial interpretation to the model decision via visual inspection.Comment: Under submission to Elsevier journa

Gaetano Raffaele - One of the best experts on this subject based on the ideXlab platform.

  • Weakly Supervised learning for land cover mapping of satellite image time series via attention-based CNN
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Ienco Dino, Gaetano Raffaele, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto
    Abstract:

    International audienceThe unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at Coarse Granularity level carrying out inexact and weak knowledge that can affect the whole process.To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the Coarse Granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions

  • Weakly supervised learning for landcover mapping of satellite image timeseries via attention-based CNN
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Ienco Dino, Gaetano Raffaele, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto
    Abstract:

    The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at Coarse Granularity level carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the Coarse Granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions

  • Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation
    2020
    Co-Authors: Ienco Dino, Gbodjo, Yawogan Jean Eudes, Interdonato Roberto, Gaetano Raffaele
    Abstract:

    Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data (series of images with high revisit time period on the same geographical area) is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich and complex image data. One of the main task associated to SITS data analysis is related to land cover mapping where satellite data are exploited via learning methods to recover the Earth Surface status aka the corresponding land cover classes. Due to operational constraints, the collected label information, on which machine learning strategies are trained, is often limited in volume and obtained at Coarse Granularity carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), that is able to intelligently exploit the weak supervision provided by the Coarse Granularity labels. Furthermore, our framework also produces an additional side-information that supports the model interpretability with the aim to make the black box gray. Such side-information allows to associate spatial interpretation to the model decision via visual inspection.Comment: Under submission to Elsevier journa

Zhou Xichuan - One of the best experts on this subject based on the ideXlab platform.

  • Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
    2021
    Co-Authors: Liu Haijun, Chai Yanxia, Tan Xiaoheng, Li Dong, Zhou Xichuan
    Abstract:

    In this letter, we propose a conceptually simple and effective dual-Granularity triplet loss for visible-thermal person re-identification (VT-ReID). In general, ReID models are always trained with the sample-based triplet loss and identification loss from the fine Granularity level. It is possible when a center-based loss is introduced to encourage the intra-class compactness and inter-class discrimination from the Coarse Granularity level. Our proposed dual-Granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to Coarse Granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization. Experiments on RegDB and SYSU-MM01 datasets show that with only the global features our dual-Granularity triplet loss can improve the VT-ReID performance by a significant margin. It can be a strong VT-ReID baseline to boost future research with high quality.Comment: to be published in IEEE Signal Processing Letter

  • Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
    2020
    Co-Authors: Liu Haijun, Chai Yanxia, Tan Xiaoheng, Li Dong, Zhou Xichuan
    Abstract:

    In this letter, we propose a conceptually simple and effective dual-Granularity triplet loss for visible-thermal person re-identification (VT-ReID). In general, ReID models are always trained with the sample-based triplet loss and identification loss from the fine Granularity level. It is possible when a center-based loss is introduced to encourage the intra-class compactness and inter-class discrimination from the Coarse Granularity level. Our proposed dual-Granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to Coarse Granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization. Experiments on RegDB and SYSU-MM01 datasets show that with only the global features our dual-Granularity triplet loss can improve the VT-ReID performance by a significant margin. It can be a strong VT-ReID baseline to boost future research with high quality

Heeseung Jo - One of the best experts on this subject based on the ideXlab platform.

  • superblock ftl a superblock based flash translation layer with a hybrid address translation scheme
    ACM Transactions in Embedded Computing Systems, 2010
    Co-Authors: Dawoon Jung, Jeonguk Kang, Heeseung Jo
    Abstract:

    In NAND flash-based storage systems, an intermediate software layer called a Flash Translation Layer (FTL) is usually employed to hide the erase-before-write characteristics of NAND flash memory. We propose a novel superblock-based FTL scheme, which combines a set of adjacent logical blocks into a superblock. In the proposed Superblock FTL, superblocks are mapped at Coarse Granularity, while pages inside the superblock are mapped freely at fine Granularity to any location in several physical blocks. To reduce extra storage and flash memory operations, the fine-grain mapping information is stored in the spare area of NAND flash memory. This hybrid address translation scheme has the flexibility provided by fine-grain address translation, while reducing the memory overhead to the level of Coarse-grain address translation. Our experimental results show that the proposed FTL scheme significantly outperforms previous block-mapped FTL schemes with roughly the same memory overhead.

  • a superblock based flash translation layer for nand flash memory
    Embedded Software, 2006
    Co-Authors: Jeonguk Kang, Heeseung Jo
    Abstract:

    In NAND flash-based storage systems, an intermediate software layer called a flash translation layer (FTL)is usually employed to hide the erase-before-write characteristics of NAND flash memory. This paper proposes a novel superblockbased FTL scheme, which combines a set of adjacent logical blocks into a superblock. In the proposed FTL scheme, superblocks are mapped at Coarse Granularity,while pages inside the superblock are mapped freely at fine Granularity to any location in several physical blocks. To reduce extra storage and flash memory operations, the fine-grain mapping information is stored in the spare area of NAND flash memory. This hybrid mapping technique has the flexibility provided by fine-grain address translation, while reducing the memory overhead to the level of Coarse-grain address translation. Our experimental results show that the proposed FTL scheme decreases the garbage collection overhead up to 40% compared to previous FTL schemes.