The Experts below are selected from a list of 215574 Experts worldwide ranked by ideXlab platform

Jixian Zhang - One of the best experts on this subject based on the ideXlab platform.

  • multi source Remote Sensing Data fusion status and trends
    International Journal of Image and Data Fusion, 2010
    Co-Authors: Jixian Zhang
    Abstract:

    With the fast development of Remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source Remote Sensing Data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source Data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source Remote Sensing Data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/Data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral Data fusing methods. So far, the pixel level fus...

Anna F Cord - One of the best experts on this subject based on the ideXlab platform.

  • inclusion of habitat availability in species distribution models through multi temporal Remote Sensing Data
    Ecological Applications, 2011
    Co-Authors: Anna F Cord, Dennis Rödder
    Abstract:

    In times of anthropogenic climate change and increasing rates of habitat loss in many areas of the world, spatially explicit predictions of species' ranges using species distribution models (SDMs) have become of central interest in conservation biology. Such predictions can be derived using species records from museum collections or field surveys combined with, for example, climate and/or land cover Data stored in geographic information systems (GIS). Although much attention has been paid to the application of bioclimatic Data for SDMs development, the inclusion of land cover information derived from Remote Sensing is still at the beginning. Herein, we tested for the first time whether SDMs can be improved by inclusion of seasonality information derived from multi-temporal Remote-Sensing Data. We compared models computed for eight Mexican anurans using five different sets of predictors comprising either bioclimatic or Remote-Sensing Data or combinations thereof. Our results suggested that the appropriate set of predictor variables is very much dependent on the species-specific habitat preferences and the patchiness/spatial fragmentation of the suitable habitat types. For species occupying spatially fragmented habitats, spatial predictions based on pure bioclimatic Data were less detailed. On the contrary, especially for rather generalist species, SDMs using only Remote-Sensing Data for model development tended to overpredict the species' ranges. For most species, the best strategy for SDM development was a combined model design using both climate and Remote-Sensing Data, while the best methodology of combining the two Data sets varied between species. This combined model design allowed us to incorporate the advantages of both approaches, i.e., inclusion of habitat availability using only Remote-Sensing Data and high spatial definition by using bioclimatic variables, by avoidance of the drawbacks of each of them.

Xiuping Jia - One of the best experts on this subject based on the ideXlab platform.

  • foreword to the special issue on quality improvements of Remote Sensing Data
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018
    Co-Authors: Huanfeng Shen, Xiuping Jia, Jose M Bioucasdias, Nicolas Dobigeon, Yi Cui, Fabio Pacifici
    Abstract:

    Remote Sensing Data are often degraded by many issues that may include the failure of onboard hardware, signal downlink, atmospheric conditions, and overall quality/age of the sensors (for example, in terms of signal-noise ratio or sharpness).

  • Deep Fusion of Remote Sensing Data for Accurate Classification
    IEEE Geoscience and Remote Sensing Letters, 2017
    Co-Authors: Yushi Chen, Pedram Ghamisi, Xiuping Jia
    Abstract:

    The multisensory fusion of Remote Sensing Data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging Data. Then, a fully connected DNN is designed to fuse the heterogeneous features obtained by the previous CNNs. Through the aforementioned deep networks, one can extract the discriminant and invariant features of Remote Sensing Data, which are useful for further processing. At last, logistic regression is used to produce the final classification results. Dropout and batch normalization strategies are adopted in the deep fusion framework to further improve classification accuracy. The obtained results reveal that the proposed deep fusion model provides competitive results in terms of classification accuracy. Furthermore, the proposed deep learning idea opens a new window for future Remote Sensing Data fusion.

Dennis Rödder - One of the best experts on this subject based on the ideXlab platform.

  • inclusion of habitat availability in species distribution models through multi temporal Remote Sensing Data
    Ecological Applications, 2011
    Co-Authors: Anna F Cord, Dennis Rödder
    Abstract:

    In times of anthropogenic climate change and increasing rates of habitat loss in many areas of the world, spatially explicit predictions of species' ranges using species distribution models (SDMs) have become of central interest in conservation biology. Such predictions can be derived using species records from museum collections or field surveys combined with, for example, climate and/or land cover Data stored in geographic information systems (GIS). Although much attention has been paid to the application of bioclimatic Data for SDMs development, the inclusion of land cover information derived from Remote Sensing is still at the beginning. Herein, we tested for the first time whether SDMs can be improved by inclusion of seasonality information derived from multi-temporal Remote-Sensing Data. We compared models computed for eight Mexican anurans using five different sets of predictors comprising either bioclimatic or Remote-Sensing Data or combinations thereof. Our results suggested that the appropriate set of predictor variables is very much dependent on the species-specific habitat preferences and the patchiness/spatial fragmentation of the suitable habitat types. For species occupying spatially fragmented habitats, spatial predictions based on pure bioclimatic Data were less detailed. On the contrary, especially for rather generalist species, SDMs using only Remote-Sensing Data for model development tended to overpredict the species' ranges. For most species, the best strategy for SDM development was a combined model design using both climate and Remote-Sensing Data, while the best methodology of combining the two Data sets varied between species. This combined model design allowed us to incorporate the advantages of both approaches, i.e., inclusion of habitat availability using only Remote-Sensing Data and high spatial definition by using bioclimatic variables, by avoidance of the drawbacks of each of them.

Joan Serra-sagristà - One of the best experts on this subject based on the ideXlab platform.

  • Multilevel Split Regression Wavelet Analysis for Lossless Compression of Remote Sensing Data
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Sara Álvarez-cortés, Joan Bartrina-rapesta, Joan Serra-sagristà
    Abstract:

    Spectral redundancy is a key element to be exploited in compression of Remote Sensing Data. Combined with an entropy encoder, it can achieve competitive lossless coding performance. One of the latest techniques to decorrelate the spectral signal is the regression wavelet analysis (RWA). RWA applies a wavelet transform in the spectral domain and estimates the detail coefficients through the approximation coefficients using linear regression. RWA was originally coupled with JPEG 2000. This letter introduces a novel coding approach, where RWA is coupled with the predictor of CCSDS-123.0-B-1 standard and a lightweight contextual arithmetic coder. In addition, we also propose a smart strategy to select the number of RWA decomposition levels that maximize the coding performance. Experimental results indicate that, on average, the obtained coding gains vary between 0.1 and 1.35 bits-per-pixel-per-component compared with the other state-of-the-art coding techniques.