The Experts below are selected from a list of 20469 Experts worldwide ranked by ideXlab platform

Ran Tao - One of the best experts on this subject based on the ideXlab platform.

  • orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency Channel features
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran Tao
    Abstract:

    With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in the frequency domain and the original spatial Channel features (e.g., Color Channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.

  • orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency Channel features
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran Tao
    Abstract:

    With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in frequency domain and the original spatial Channel features (e.g., Color Channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.

Xiaoou Tang - One of the best experts on this subject based on the ideXlab platform.

  • single image haze removal using dark Channel prior
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
    Co-Authors: Jian Sun, Xiaoou Tang
    Abstract:

    In this paper, we propose a simple but effective image prior-dark Channel prior to remove haze from a single input image. The dark Channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one Color Channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.

  • single image haze removal using dark Channel prior
    Computer Vision and Pattern Recognition, 2009
    Co-Authors: Jian Sun, Xiaoou Tang
    Abstract:

    In this paper, we propose a simple but effective image prior - dark Channel prior to remove haze from a single input image. The dark Channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one Color Channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal.

Danfeng Hong - One of the best experts on this subject based on the ideXlab platform.

  • orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency Channel features
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran Tao
    Abstract:

    With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in the frequency domain and the original spatial Channel features (e.g., Color Channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.

  • orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency Channel features
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran Tao
    Abstract:

    With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in frequency domain and the original spatial Channel features (e.g., Color Channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.

Jian Sun - One of the best experts on this subject based on the ideXlab platform.

  • single image haze removal using dark Channel prior
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
    Co-Authors: Jian Sun, Xiaoou Tang
    Abstract:

    In this paper, we propose a simple but effective image prior-dark Channel prior to remove haze from a single input image. The dark Channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one Color Channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.

  • single image haze removal using dark Channel prior
    Computer Vision and Pattern Recognition, 2009
    Co-Authors: Jian Sun, Xiaoou Tang
    Abstract:

    In this paper, we propose a simple but effective image prior - dark Channel prior to remove haze from a single input image. The dark Channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one Color Channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal.

Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.

  • orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency Channel features
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran Tao
    Abstract:

    With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in the frequency domain and the original spatial Channel features (e.g., Color Channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.

  • orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency Channel features
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Ran Tao
    Abstract:

    With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse Channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-frequency Channel feature (SFCF) by jointly considering the rotation-invariant Channel features constructed in frequency domain and the original spatial Channel features (e.g., Color Channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled Channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.