FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis

González, Carlos Villaseca; Mozos, Daniel; Resano, Javier; Plaza, Antonio

doi:10.1109/tgrs.2011.2171693

Cited by 62 publications

(35 citation statements)

References 30 publications

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“…If we consider the area of spectral unmixing, implementation of endmember extraction algorithms using a Xilinx Virtex-4 FPGA have been recently described in [185]. This FPGA model is similar to radiation-hardened FPGAs certified for space operation.…”

Section: Fpgas For Hyperspectral Processingmentioning

confidence: 99%

“…Similarly FPGA implementations of abundance estimation algorithms have also been described in [186]. This implementation was tested in the same FPGA used in [185], and achieved a speedup factor of 10x when processing the AVIRIS Cuprite scene and over 12# when it comes to the AVIRIS Jasper Ridge scene. Authors also reach the conclusion that, using FPGAs, the execution time scales linearly with the size of the image.…”

Section: Fpgas For Hyperspectral Processingmentioning

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

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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.

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Section: Fpgas For Hyperspectral Processingmentioning

confidence: 99%

Section: Fpgas For Hyperspectral Processingmentioning

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

“…All correlation logics are processing in parallel. To calculate the range, the correlation logic with valid peak detection is taken and the delay of the reference PRBS of the respective logic is noticed [10]. Table 1 depicts the simulated results and draws a comparison between the previous result and those obtained through our algorithm.the results are increasing and show a significant improvement over the previous result.…”

Section: F Digital Correlatormentioning

confidence: 90%

Hdl Implementation of Algorithm To Detect The Proximity of A Moving Target

Sangeeta¹

2017

IJRASET

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Radar Signal Processors heavily possess the capabilities of traditional microprocessors based signal processing systems. Higher performance systems using custom-made silicon, cost too much for the typically small production volumes and are not flexible enough for research applications. The performance of custom-made silicon while maintaining the economies, the Field Programmable Gate Arrays offer too much flexibility in comparison to traditional microprocessor-based solutions. The recent device possess the density and performance to realize a complete radar signal processor in a single FPGA, including complex down conversion of the IF to baseband. The commercially available FPGA boards eliminated the specific chips required for the down conversion and matched filtering. Thus, the use of FPGA has been increased extensively. In the field of motion detection, the researchers have shown various deviation detection methods for efficient detection of small moving targets. These algorithms can detect moving objects deviation in a properly defined attribute space. The deviation is defined as an object distinct from the objects in its neighbourhood. In this work, we are going to increase the proximity range for the moving targets. The BPSK Modulation is going to be used in this for getting the correct and enhanced input signals. The work completely relates to a Radar system where speed and accuracy could not be ignored. In this work, an algorithm has been implemented to detect the proximity of a target. The major challenges in developing this algorithm are to detect the target when it is coming towards Radar within a specified range (0 to 55 meters) with a range resolution of 3.4 meters.

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“…The runtime of the RX and DWEST families that use the DR step is acceptable; these methods can be used in real-time applications by using parallel processing or hardware implementation of algorithms using field programmable gate array (FPGA) [32,33]. Of all the methods, the FFT-RX has the best runtime: its runtime is about 124 times better than the slowest method, the Kernel-RX.…”

Section: Runtime Evaluationmentioning

confidence: 99%

Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images

Zare-Baghbidi

Homayouni²,

Jamshidi

et al. 2015

JAIDM

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Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison with their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of three popular linear dimensionality reduction methods on the performance of three benchmark anomaly detection algorithms. The Principal Component Analysis (PCA), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) as DR methods, act as pre-processing step for AD algorithms. The assessed AD algorithms are ReedXiaoli (RX), Kernel-based versions of the RX (Kernel-RX) and Dual Window-Based Eigen Separation Transform (DWEST). The AD methods have been applied to two hyperspectral datasets acquired by both the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Mapper (HyMap) sensors. The evaluation of experiments has been done using Receiver Operation Characteristic (ROC) curve, visual investigation and runtime of the algorithms. Experimental results show that the DR methods can significantly improve the detection performance of the RX method. The detection performance of neither the Kernel-RX method nor the DWEST method changes when using the proposed methods. Moreover, these DR methods increase the runtime of the RX and DWEST significantly and make them suitable to be implemented in real time applications.

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FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis

Cited by 62 publications

References 30 publications

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Hdl Implementation of Algorithm To Detect The Proximity of A Moving Target

Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images

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