The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Naoto Yokoya - One of the best experts on this subject based on the ideXlab platform.
-
graph induced aligned learning on subspaces for hyperspectral and Multispectral Data
IEEE Transactions on Geoscience and Remote Sensing, 2021Co-Authors: Danfeng Hong, Naoto Yokoya, Jian Kang, Jocelyn ChanussotAbstract:In this article, we have great interest in investigating a common but practical issue in remote sensing (RS)—can a limited amount of one information-rich (or high-quality) Data, e.g., hyperspectral (HS) image, improve the performance of a classification task using a large amount of another information-poor (low-quality) Data, e.g., Multispectral (MS) image? This question leads to a typical cross-modality feature learning. However, classic cross-modality representation learning approaches, e.g., manifold alignment, remain limited in effectively and efficiently handling such problems that the Data from high-quality modality are largely absent. For this reason, we propose a novel graph-induced aligned learning (GiAL) framework by 1) adaptively learning a unified graph (further yielding a Laplacian matrix) from the Data in order to align multimodality Data (MS-HS Data) into a latent shared subspace; 2) simultaneously modeling two regression behaviors with respect to labels and pseudo-labels under a multitask learning paradigm; and 3) dramatically updating the pseudo-labels according to the learned graph and refeeding the latest pseudo-labels into model learning of the next round. In addition, an optimization framework based on the alternating direction method of multipliers (ADMMs) is devised to solve the proposed GiAL model. Extensive experiments are conducted on two MS-HS RS Data sets, demonstrating the superiority of the proposed GiAL compared with several state-of-the-art methods.
-
learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification
Isprs Journal of Photogrammetry and Remote Sensing, 2019Co-Authors: Naoto Yokoya, Danfeng Hong, Jocelyn Chanussot, Xiao Xiang ZhuAbstract:Abstract In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community—can a limited amount of highly-discriminative (e.g., hyperspectral) training Data improve the performance of a classification task using a large amount of poorly-discriminative (e.g., Multispectral) Data? Traditional semi-supervised manifold alignment methods do not perform sufficiently well for such problems, since the hyperspectral Data is very expensive to be largely collected in a trade-off between time and efficiency, compared to the Multispectral Data. To this end, we propose a novel semi-supervised cross-modality learning framework, called learnable manifold alignment (LeMA). LeMA learns a joint graph structure directly from the Data instead of using a given fixed graph defined by a Gaussian kernel function. With the learned graph, we can further capture the Data distribution by graph-based label propagation, which enables finding a more accurate decision boundary. Additionally, an optimization strategy based on the alternating direction method of multipliers (ADMM) is designed to solve the proposed model. Extensive experiments on two hyperspectral-Multispectral Datasets demonstrate the superiority and effectiveness of the proposed method in comparison with several state-of-the-art methods.
-
hyperspectral and Multispectral Data fusion a comparative review of the recent literature
IEEE Geoscience and Remote Sensing Magazine, 2017Co-Authors: Naoto Yokoya, Claas Grohnfeldt, Jocelyn ChanussotAbstract:In recent years, enormous efforts have been made to design image-processing algorithms to enhance the spatial resolution of hyperspectral (HS) imagery. One of the most commonly addressed problems is the fusion of HS Data with higher spatial resolution Multispectral (MS) Data. Various techniques have been proposed to solve this Data-fusion problem based on different theories, including component substitution (CS), multiresolution analysis (MRA), spectral unmixing, and Bayesian probability. This article presents a comparative review of those HS-MS fusion techniques with extensive experiments. Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually. Eight Data sets featuring different geographical and sensor characteristics are used in the experiments to evaluate the generalizability and versatility of the fusion algorithms. To maximize the fairness and transparency of this comparison, publicly available source codes are used, and parameters are individually tuned for maximum performance.
-
cross calibration for Data fusion of eo 1 hyperion and terra aster
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013Co-Authors: Naoto Yokoya, N Mayumi, Akira IwasakiAbstract:The Data fusion of low spatial-resolution hyperspectral and high spatial-resolution Multispectral images enables the production of high spatial-resolution hyperspectral Data with small spectral distortion. EO-1/Hyperion is the world's first hyperspectral sensor. It was launched in 2001 and has a similar orbit to Terra/ASTER. In this work, we apply hyperspectral and Multispectral Data fusion to EO-1/Hyperion and Terra/ASTER Datasets by the preprocessing of Datasets and the onboard cross-calibration of sensor characteristics. The relationship of the spectral response function is determined by convex optimization by comparing hyperspectral and Multispectral images over the same spectral range. After accurate image registration, the relationship of the point spread function is obtained by estimating a matrix that acts as Gaussian blur filter between two images. Two pansharpening-based methods and one unmixing-based method are adopted for hyperspectral and Multispectral Data fusion and their properties are investigated.
-
coupled nonnegative matrix factorization unmixing for hyperspectral and Multispectral Data fusion
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Naoto Yokoya, Takehisa Yairi, Akira IwasakiAbstract:Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution Multispectral Data to produce fused Data with high spatial and spectral resolutions. Both hyperspectral and Multispectral Data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two Data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image Data sets demonstrate that the CNMF algorithm can produce high-quality fused Data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.
Shoshana Z. Weider - One of the best experts on this subject based on the ideXlab platform.
-
individual lava flow thicknesses in oceanus procellarum and mare serenitatis determined from clementine Multispectral Data
Icarus, 2010Co-Authors: Shoshana Z. Weider, Ian A. Crawford, Katherine H. JoyAbstract:Abstract We use Multispectral reflectance Data from the lunar Clementine mission to investigate the impact ejecta deposits of simple craters in two separate lunar mare basalt regions, one in Oceanus Procellarum and one in Mare Serenitatis. Over 100 impact craters are studied, and for a number of these we observe differences between the TiO 2 (and FeO) contents of their ejecta deposits and the lava flow units in which they are located. We demonstrate that, in the majority of cases, these differences cannot plausibly be attributed to uncorrected maturity effects. These observations, coupled with morphometric crater relationships that provide maximum crater excavation depths, allow the investigation of sub-surface lava flow stratigraphy. We provide estimated average thicknesses for a number of lava flow units in the two study regions, ranging from ∼80 m to ∼600 m. In the case of the Serenitatis study area, our results are consistent with the presence of sub-surface horizons inferred from recent radar sounding measurements from the JAXA Kaguya spacecraft. The average lava flow thicknesses we obtain are used to make estimates of the average flux of volcanic material in these regions. These are in broad agreement with previous studies, suggesting that the variation in mare basalt types we observe with depth is similar to the lateral variations identified at the surface.
-
individual lava flow thicknesses in oceanus procellarum and mare serenitatis determined from clementine Multispectral Data
Icarus, 2010Co-Authors: Shoshana Z. Weider, Ian A. Crawford, Katherine H. JoyAbstract:We use Multispectral reflectance Data from the lunar Clementine mission to investigate the impact ejecta deposits of simple craters in two separate lunar mare basalt regions, one in Oceanus Procellarum and one in Mare Serenitatis. Over 100 impact craters are studied, and for a number of these we observe differences between the TiO2 (and FeO) contents of their ejecta deposits and the lava flow units in which they are located. We demonstrate that, in the majority of cases, these differences cannot plausibly be attributed to uncorrected maturity effects. These observations, coupled with morphometric crater relationships that provide maximum crater excavation depths, allow the investigation of sub-surface lava flow stratigraphy. We provide estimated average thicknesses for a number of lava flow units in the two study regions, ranging from similar to 80 m to similar to 600 m. In the case of the Serenitatis study area, our results are consistent with the presence of sub-surface horizons inferred from recent radar sounding measurements from the JAXA Kaguya spacecraft. The average lava flow thicknesses we obtain are used to make estimates of the average flux of volcanic material in these regions. These are in broad agreement with previous studies, suggesting that the variation in mare basalt types we observe with depth is similar to the lateral variations identified at the surface. (C) 2010 Elsevier Inc. All rights reserved.
Akira Iwasaki - One of the best experts on this subject based on the ideXlab platform.
-
cross calibration for Data fusion of eo 1 hyperion and terra aster
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013Co-Authors: Naoto Yokoya, N Mayumi, Akira IwasakiAbstract:The Data fusion of low spatial-resolution hyperspectral and high spatial-resolution Multispectral images enables the production of high spatial-resolution hyperspectral Data with small spectral distortion. EO-1/Hyperion is the world's first hyperspectral sensor. It was launched in 2001 and has a similar orbit to Terra/ASTER. In this work, we apply hyperspectral and Multispectral Data fusion to EO-1/Hyperion and Terra/ASTER Datasets by the preprocessing of Datasets and the onboard cross-calibration of sensor characteristics. The relationship of the spectral response function is determined by convex optimization by comparing hyperspectral and Multispectral images over the same spectral range. After accurate image registration, the relationship of the point spread function is obtained by estimating a matrix that acts as Gaussian blur filter between two images. Two pansharpening-based methods and one unmixing-based method are adopted for hyperspectral and Multispectral Data fusion and their properties are investigated.
-
coupled nonnegative matrix factorization unmixing for hyperspectral and Multispectral Data fusion
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Naoto Yokoya, Takehisa Yairi, Akira IwasakiAbstract:Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution Multispectral Data to produce fused Data with high spatial and spectral resolutions. Both hyperspectral and Multispectral Data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two Data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image Data sets demonstrate that the CNMF algorithm can produce high-quality fused Data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.
Katherine H. Joy - One of the best experts on this subject based on the ideXlab platform.
-
individual lava flow thicknesses in oceanus procellarum and mare serenitatis determined from clementine Multispectral Data
Icarus, 2010Co-Authors: Shoshana Z. Weider, Ian A. Crawford, Katherine H. JoyAbstract:Abstract We use Multispectral reflectance Data from the lunar Clementine mission to investigate the impact ejecta deposits of simple craters in two separate lunar mare basalt regions, one in Oceanus Procellarum and one in Mare Serenitatis. Over 100 impact craters are studied, and for a number of these we observe differences between the TiO 2 (and FeO) contents of their ejecta deposits and the lava flow units in which they are located. We demonstrate that, in the majority of cases, these differences cannot plausibly be attributed to uncorrected maturity effects. These observations, coupled with morphometric crater relationships that provide maximum crater excavation depths, allow the investigation of sub-surface lava flow stratigraphy. We provide estimated average thicknesses for a number of lava flow units in the two study regions, ranging from ∼80 m to ∼600 m. In the case of the Serenitatis study area, our results are consistent with the presence of sub-surface horizons inferred from recent radar sounding measurements from the JAXA Kaguya spacecraft. The average lava flow thicknesses we obtain are used to make estimates of the average flux of volcanic material in these regions. These are in broad agreement with previous studies, suggesting that the variation in mare basalt types we observe with depth is similar to the lateral variations identified at the surface.
-
individual lava flow thicknesses in oceanus procellarum and mare serenitatis determined from clementine Multispectral Data
Icarus, 2010Co-Authors: Shoshana Z. Weider, Ian A. Crawford, Katherine H. JoyAbstract:We use Multispectral reflectance Data from the lunar Clementine mission to investigate the impact ejecta deposits of simple craters in two separate lunar mare basalt regions, one in Oceanus Procellarum and one in Mare Serenitatis. Over 100 impact craters are studied, and for a number of these we observe differences between the TiO2 (and FeO) contents of their ejecta deposits and the lava flow units in which they are located. We demonstrate that, in the majority of cases, these differences cannot plausibly be attributed to uncorrected maturity effects. These observations, coupled with morphometric crater relationships that provide maximum crater excavation depths, allow the investigation of sub-surface lava flow stratigraphy. We provide estimated average thicknesses for a number of lava flow units in the two study regions, ranging from similar to 80 m to similar to 600 m. In the case of the Serenitatis study area, our results are consistent with the presence of sub-surface horizons inferred from recent radar sounding measurements from the JAXA Kaguya spacecraft. The average lava flow thicknesses we obtain are used to make estimates of the average flux of volcanic material in these regions. These are in broad agreement with previous studies, suggesting that the variation in mare basalt types we observe with depth is similar to the lateral variations identified at the surface. (C) 2010 Elsevier Inc. All rights reserved.
C H Davis - One of the best experts on this subject based on the ideXlab platform.
-
a combined fuzzy pixel based and object based approach for classification of high resolution Multispectral Data over urban areas
IEEE Transactions on Geoscience and Remote Sensing, 2003Co-Authors: A K Shackelford, C H DavisAbstract:In this paper, we present an object-based approach for urban land cover classification from high-resolution Multispectral image Data that builds upon a pixel-based fuzzy classification approach. This combined pixel/object approach is demonstrated using pan-sharpened Multispectral IKONOS imagery from dense urban areas. The fuzzy pixel-based classifier utilizes both spectral and spatial information to discriminate between spectrally similar road and building urban land cover classes. After the pixel-based classification, a technique that utilizes both spectral and spatial heterogeneity is used to segment the image to facilitate further object-based classification. An object-based fuzzy logic classifier is then implemented to improve upon the pixel-based classification by identifying one additional class in dense urban areas: nonroad, nonbuilding impervious surface. With the fuzzy pixel-based classification as input, the object-based classifier then uses shape, spectral, and neighborhood features to determine the final classification of the segmented image. Using these techniques, the object-based classifier is able to identify buildings, impervious surface, and roads in dense urban areas with 76%, 81%, and 99% classification accuracies, respectively.
-
a hierarchical fuzzy classification approach for high resolution Multispectral Data over urban areas
IEEE Transactions on Geoscience and Remote Sensing, 2003Co-Authors: A K Shackelford, C H DavisAbstract:In this paper, we investigate the usefulness of high-resolution Multispectral satellite imagery for classification of urban and suburban areas and present a fuzzy logic methodology to improve classification accuracy. Panchromatic and Multispectral IKONOS image Datasets are analyzed for two urban locations in this study. Both Multispectral and pan-sharpened Multispectral images are first classified using a traditional maximum-likelihood approach. Maximum-likelihood classification accuracies between 79% to 87% were achieved with significant misclassification error between the spectrally similar Road and Building urban land cover types. A number of different texture measures were investigated, and a length-width contextual measure is developed. These spatial measures were used to increase the discrimination between spectrally similar classes, thereby yielding higher accuracy urban land cover maps. Finally, a hierarchical fuzzy classification approach that makes use of both spectral and spatial information is presented. This technique is shown to increase the discrimination between spectrally similar urban land cover classes and results in classification accuracies that are 8% to 11% larger than those from the traditional maximum-likelihood approach.
