Parallel Hyperspectral Image and Signal Processing [Applications Corner]

Plaza, Javier; Paz, Abel; Sánchez, Sergio

doi:10.1109/msp.2011.940409

Cited by 129 publications

(56 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another strategy for feature extraction has been grouping of neighboring bands, using techniques such as the weighted sum or average of each group [81]. A free Matlab toolbox for linear and nonlinear feature extraction methods is simFEAT 12 .…”

Section: A Feature Miningmentioning

confidence: 99%

“…Another important issue is the extremely high dimensionality and size of the data, resulting from the improved spatial, spectral and temporal resolutions provided by hyperspectral instruments. This demands fast computing solutions that can accelerate the interpretation and efficient exploitation of hyperspectral data sets in various applications [12]. For example, it has been estimated by the NASA's Jet Propulsion Laboratory (JPL) that a volume of 4.5 TBytes of data will be daily produced by HyspIRI (1630 TBytes per year).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Bioucas-Dias¹,

Plaza

Camps‐Valls

et al. 2013

IEEE Geosci. Remote Sens. Mag.

Self Cite

1,689

766

View full text Add to dashboard Cite

Abstract-Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

show abstract

Section: A Feature Miningmentioning

confidence: 99%

mentioning

confidence: 99%

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Bioucas-Dias¹,

Plaza

Camps‐Valls

et al. 2013

IEEE Geosci. Remote Sens. Mag.

Self Cite

1,689

766

View full text Add to dashboard Cite

show abstract

“…With the rapid development of remote sensing, it has become much easier to obtain high-resolution images [1][2][3]. The ever-increasing amount of data places more emphasis on automated interpretation of remote sensing images [2,4].…”

Section: Introductionmentioning

confidence: 99%

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

et al. 2018

View full text Add to dashboard Cite

Abstract:The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

show abstract

“…Unfortunately, the high dimensionality of the hyperspectral data provided by modern sensors as well as the inherent computational complexity of the HFC-VD algorithm clearly make its use prohibitive for applications under real-time or near real-time constraints. Hence, the utilization of high-performance computing platforms to accelerate the process of unmixing a hyperspectral image becomes mandatory for such scenarios [5][6][7][8]. For this purpose, reconfigurable hardware solutions such as fieldprogrammable gate arrays (FPGAs) have been consolidated during the last years as one of the standard choices for the fast processing of hyperspectral remotely sensed images due to their smaller size, weight and power consumption when compared with other high-performance computing systems [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A novel FPGA-based architecture for the estimation of the virtual dimensionality in remotely sensed hyperspectral images

González

López

Mozos

et al. 2015

J Real-Time Image Proc

View full text Add to dashboard Cite

A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular ways to determine the number of endmembers is by estimating the virtual dimensionality (VD) of the hyperspectral image using the well-known Harsanyi-Farrand-Chang (HFC) method. Due to the complexity and high dimensionality of hyperspectral scenes, this task is computationally expensive. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present the first FPGA design for the HFC-VD algorithm. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASA's Airborne Visible InfraRed Imaging Spectrometer over the Cuprite mining district in Nevada and the World Trade Center in New York. Experimental results demonstrate that our hardware version of the HFC-VD algorithm can significantly outperform an equivalent software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing. Most important, our implementation exhibits real-time performance with regard to the time that the hyperspectral instrument takes to collect the image data.

show abstract

Parallel Hyperspectral Image and Signal Processing [Applications Corner]

Cited by 129 publications

References 11 publications

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Hyperspectral Remote Sensing Data Analysis and Future Challenges

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

A novel FPGA-based architecture for the estimation of the virtual dimensionality in remotely sensed hyperspectral images

Contact Info

Product

Resources

About