The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform

Yen-wei Chen - One of the best experts on this subject based on the ideXlab platform.

  • Generalized Aggregation of Sparse Coded Multi-Spectra for Satellite Scene Classification
    ISPRS International Journal of Geo-Information, 2017
    Co-Authors: Xian-hua Han, Yen-wei Chen
    Abstract:

    Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of Spectral bands than a general image, thereby permitting the multi-Spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-Spectral analysis and explores the middle-level statistics of Spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-Spectral analysis firstly learns the multi-Spectral prototypes (codebook) for representing any pixel-wise Spectral data, and then, based on the learned codebook, a sparse coded Spectral Vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded Spectral Vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods, but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is very impressive compared with the state-of-the-art methods for satellite scene classification.

  • Generalized Aggregation of Sparse Coded Multi-Spectral for Satellite Scene Classification
    2017
    Co-Authors: Xian-hua Han, Yen-wei Chen
    Abstract:

    Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of Spectral bands than a general image, thereby permitting the multi-Spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-Spectral analysis and explores the middle-level statistics of Spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-Spectral analysis firstly learns the multi-Spectral prototypes (codebook) for representing any pixel-wise Spectral data, and then based on the learned codebook, a sparse coded Spectral Vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded Spectral Vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is much more accurate than even the deep learning framework for spatial analysis.

Xian-hua Han - One of the best experts on this subject based on the ideXlab platform.

  • Generalized Aggregation of Sparse Coded Multi-Spectra for Satellite Scene Classification
    ISPRS International Journal of Geo-Information, 2017
    Co-Authors: Xian-hua Han, Yen-wei Chen
    Abstract:

    Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of Spectral bands than a general image, thereby permitting the multi-Spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-Spectral analysis and explores the middle-level statistics of Spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-Spectral analysis firstly learns the multi-Spectral prototypes (codebook) for representing any pixel-wise Spectral data, and then, based on the learned codebook, a sparse coded Spectral Vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded Spectral Vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods, but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is very impressive compared with the state-of-the-art methods for satellite scene classification.

  • Generalized Aggregation of Sparse Coded Multi-Spectral for Satellite Scene Classification
    2017
    Co-Authors: Xian-hua Han, Yen-wei Chen
    Abstract:

    Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of Spectral bands than a general image, thereby permitting the multi-Spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-Spectral analysis and explores the middle-level statistics of Spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-Spectral analysis firstly learns the multi-Spectral prototypes (codebook) for representing any pixel-wise Spectral data, and then based on the learned codebook, a sparse coded Spectral Vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded Spectral Vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is much more accurate than even the deep learning framework for spatial analysis.

G Trianni - One of the best experts on this subject based on the ideXlab platform.

  • multitemporal settlement and population mapping from landsat using google earth engine
    International Journal of Applied Earth Observation and Geoinformation, 2015
    Co-Authors: Nirav N Patel, Emanuele Angiuli, Paolo Gamba, Andrea E Gaughan, Gianni Lisini, Forrest R Stevens, Andrew J Tatem, G Trianni
    Abstract:

    As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.

  • urban mapping in landsat images based on normalized difference Spectral Vector
    IEEE Geoscience and Remote Sensing Letters, 2014
    Co-Authors: Emanuele Angiuli, G Trianni
    Abstract:

    In the last decades the number of natural and anthropic changes affecting population worldwide has raised dramatically. This fact, coupled with the increasing world population living in urban areas, requires the development of a detailed and reliable map of global urban extent. This letter reports on a new approach for urban mapping from Landsat images, based on the Normalized Difference Spectral Vector (NDSV). This Spectral transformation allows the creation of a normalized signature that becomes peculiar of each land cover class within the scene. The urban extent classification is obtained by analyzing the NDSV data in conjunction with a Spectral Angle Mapper (SAM) based classifier. The experiments presented in this letter show the effectiveness of the proposed technique in detecting urban areas in extremely different environments. The results of the proposed methodology have been compared with the ones obtained by classifying the NDSV using other classifiers [namely, maximum likehood (ML) and support Vector machines (SVM)], and also to the results obtained by classifying the calibrated data using the ML, SVM and SAM classifiers. The NDSV+SAM approach has provided the best results, with an overall accuracy of 97%.

J. Dai - One of the best experts on this subject based on the ideXlab platform.

  • Isolated word recognition using Markov chain models
    IEEE Transactions on Speech and Audio Processing, 1995
    Co-Authors: J. Dai
    Abstract:

    The paper describes how Markov chains may be applied to speech recognition. In this application, a Spectral Vector is modeled by a state of the Markov chain, and an utterance is represented by a sequence of states. The Markov chain model (MCM) offers a substantial reduction in computation, but at the expense of a significant increase in memory requirement when compared to the hidden Markov model (HMM). Experiments on isolated word recognition show that the MCM achieved results that are comparable to those of the HMMs tested for comparison.

  • Application of Markov chains to speech recognition
    Electronics Letters, 1991
    Co-Authors: J. Dai, J.e. Tyler, I.g. Mackenzie
    Abstract:

    The Letter describes how discrete Markov chains may be applied to speech recognition. In this application, a Spectral Vector is modelled by a state of a Markov chain, and an utterance is viewed as a sequence of observed states. Experiment showed that a speech recogniser based on this Markov model not only outperforms the HMM recognisers tested for comparison, but also offers a saving in computation time.

Emanuele Angiuli - One of the best experts on this subject based on the ideXlab platform.

  • multitemporal settlement and population mapping from landsat using google earth engine
    International Journal of Applied Earth Observation and Geoinformation, 2015
    Co-Authors: Nirav N Patel, Emanuele Angiuli, Paolo Gamba, Andrea E Gaughan, Gianni Lisini, Forrest R Stevens, Andrew J Tatem, G Trianni
    Abstract:

    As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.

  • urban mapping in landsat images based on normalized difference Spectral Vector
    IEEE Geoscience and Remote Sensing Letters, 2014
    Co-Authors: Emanuele Angiuli, G Trianni
    Abstract:

    In the last decades the number of natural and anthropic changes affecting population worldwide has raised dramatically. This fact, coupled with the increasing world population living in urban areas, requires the development of a detailed and reliable map of global urban extent. This letter reports on a new approach for urban mapping from Landsat images, based on the Normalized Difference Spectral Vector (NDSV). This Spectral transformation allows the creation of a normalized signature that becomes peculiar of each land cover class within the scene. The urban extent classification is obtained by analyzing the NDSV data in conjunction with a Spectral Angle Mapper (SAM) based classifier. The experiments presented in this letter show the effectiveness of the proposed technique in detecting urban areas in extremely different environments. The results of the proposed methodology have been compared with the ones obtained by classifying the NDSV using other classifiers [namely, maximum likehood (ML) and support Vector machines (SVM)], and also to the results obtained by classifying the calibrated data using the ML, SVM and SAM classifiers. The NDSV+SAM approach has provided the best results, with an overall accuracy of 97%.