High performance GPU computing based approaches for oil spill detection from multi-temporal remote sensing data

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

“…In [102], this framework was used to address the time-series quantitative retrieval problem, which could be implemented using multilevel parallelism including finegrained parallelism (using the GPU) and coarse-grained parallelism (across CPUs). A similar study based on MPI + CUDA was presented in [103]. The results showed that the combination of MPI and CUDA in the discussed implementation could significantly speed up the oil detection process and provide a rapid response.…”

Classification of Remote Sensing Data With Morphological Attribute Profiles: A decade of advances

“…Although the aforementioned four attributes are by far the most widely encountered in the state of the art, APs can accommodate (from a theoretical point of view) a vast pool of attributes. Examples include entropy, homogeneity [7], as well as the diameter of equivalent circle and area of convex hull for automatic threshold selection [64]; complexity (perimeter over area) [65]; perimeter and area of bounding box used to evaluate threshold-free APs [66]; solidity (area over area of convex hull) and orientation (between the major axis of the convex hull and the x-axis) [67]; Cov (Coefficient of variation) and NRCS (Normalized Radar Cross Section) tailored for SAR images [33], [39], where Cov is the ratio of the standard deviation divided by the mean value of pixel intensities, and NRCS, expressed in decibels, is the radar cross section per unit area of surface. Furthermore, in [68], it has been observed that when dealing with multiband input, one can extend the pool of attribute measures to include multi-dimensional functions exploiting all available bands simultaneously and two new attributes have been proposed: higher-dimensional spread and dispersion.…”

High-Performance Time-Series Quantitative Retrieval From Satellite Images on a GPU Cluster

“…During the past several years, GPU has evolved into a highly parallel, multithreaded, many-core processor with tremendous computational horsepower and very high memory bandwidth, which has been used to accelerate some remote sensing applications. Many research demonstrated that hyperspectral RS data processing such as hyperspectral unmixing, band selection, image classification, automatic target detection [17][18][19][20] and real-time missions such as oil spill detection [21], on-board image data processing [22,23] can benefit from GPUs. In addition, a few efforts have been made to accelerate quantitative RS retrieval applications, in which in general complex partial differential equations need to be solved.…”