Parallel real-time virtual dimensionality estimation for hyperspectral images

Torti, Emanuele; Fontanella, Alessandro

doi:10.1007/s11554-017-0703-6

Cited by 8 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 3 shows the specific use of the least square method. In CNMF, the virtual dimensionality (VD) algorithm [47] analyzes the typical eigenvalue of HS data by PCA and estimates the number of spectrally distinct signal sources in the data. The number of spectrally distinct signal sources is the initial number of endmembers (K).…”

Section: Parameter Settingsmentioning

confidence: 99%

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

et al. 2020

View full text Add to dashboard Cite

The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data.

show abstract

Section: Parameter Settingsmentioning

confidence: 99%

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

et al. 2020

View full text Add to dashboard Cite

show abstract

“…As a result of the increasing demand for high-performance graphic computing and deep learning computing, the computing power of graphic processing units (GPUs) has made great achievements and been widely applied for general purposes in recent years. Considering the excellent features, e.g., light weight, small size, and low cost, GPUs are widely utilized to improve the computing performance in hyperspectral data applications [19,20,21,22,23,24,34,35,36,37,38,39]. Compute unified device architecture (CUDA) (), introduced by NVidia corporation, provides a development environment for creating high performance GPU-accelerated applications.…”

Section: Multi-gpus-based Parallel Design Of Acoeementioning

confidence: 99%

Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images

Gao

Sun

Zhang

et al. 2019

Sensors

View full text Add to dashboard Cite

Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances in each pixel of the scene. Many algorithms have been proposed to extract endmembers automatically, which is a critical step in the spectral unmixing chain. In recent years, the ant colony optimization (ACO) algorithm has been developed for endmember extraction from hyperspectral data, which was regarded as a combinatorial optimization problem. Although the ACO for endmember extraction (ACOEE) can acquire accurate endmember results, its high computational complexity has limited its application in the hyperspectral data analysis. The GPUs parallel computing technique can be utilized to improve the computational performance of ACOEE, but the architecture of GPUs determines that the ACOEE should be redesigned to take full advantage of computing resources on GPUs. In this paper, a multiple sub-ant-colony-based parallel design of ACOEE was proposed, in which an innovative mechanism of local pheromone for sub-ant-colonies is utilized to enable ACOEE to be preferably executed on the multi-GPU system. The proposed method can avoid much synchronization among different GPUs to affect the computational performance improvement. The experiments on two real hyperspectral datasets demonstrated that the computational performance of ACOEE significantly benefited from the proposed methods.

show abstract

“…Botella et al [ 19 ] proposed an architecture for a neuromorphic, robust optical flow based on a FPGA, which was applied in a complicated environment. Multi-core processors and graphic processing units (GPUs) for achieving real-time performance of the Harsanyi–Farrand–Chang (HFC) method for a virtual dimensionality (VD) algorithm was proposed for unmixing [ 20 ]. Carlos et al presented the first FPGA design for the HFC-VD algorithm to realize unmixing [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Shi

Zhuang

et al. 2018

Sensors

View full text Add to dashboard Cite

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

show abstract

Parallel real-time virtual dimensionality estimation for hyperspectral images

Cited by 8 publications

References 12 publications

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Contact Info

Product

Resources

About