FPGA Implementation of the Pixel Purity Index Algorithm for Remotely Sensed Hyperspectral Image Analysis (original) (raw)
Related papers
2005
Hyperspectral imagery is a class of image data which is used in many scientific areas, most notably, medical imaging and remote sensing. It is characterized by a wealth of spatial and spectral information. Over the last years, many algorithms have been developed with the purpose of finding "spectral endmembers," which are assumed to be pure signatures in remotely sensed hyperspectral data sets. Such pure signatures can then be used to estimate the abundance or concentration of materials in mixed pixels, thus allowing sub-pixel analysis which is crucial in many remote sensing applications due to current sensor optics and configuration. One of the most popular endmember extraction algorithms has been the pixel purity index (PPI), available from Kodak's Research Systems ENVI software package. This algorithm is very time consuming, a fact that has generally prevented its exploitation in valid response times in a wide range of applications, including environmental monitoring, military applications or hazard and threat assessment/tracking (including wildland fire detection, oil spill mapping and chemical and biological standoff detection). Field programmable gate arrays (FPGAs) are hardware components with millions of gates. Their reprogrammability and high computational power makes them particularly attractive in remote sensing applications which require a response in near real-time. In this paper, we present an FPGA design for implementation of PPI algorithm which takes advantage of a recently developed fast PPI (FPPI) algorithm that relies on software-based optimization. The proposed FPGA design represents our first step toward the development of a new reconfigurable system for fast, onboard analysis of remotely sensed hyperspectral imagery.
FPGA for Computing the Pixel Purity Index Algorithm on Hyperspectral Images
2010
The pixel purity index algorithm is employed in remote sensing for analyzing hyperspectral images. A single pixel usually covers several different materials, and its observed spectrum can be expressed as a linear combination of a few pure spectral signatures. This algorithm tries to identify these pure spectra. In this paper, we present a Field Programmable Gate Array implementation of the algorithm, which has been implemented on a Virtex-II PRO XC2VP30 and on a Virtex-4 XC4VFX60 FPGAs and evaluated using the well-known “Cuprite” image (a standard benchmark in hyperspectral imaging applications). Our experimental results demonstrate that a hardware version of the PPI algorithm can significantly outperform an equivalent software version of the algorithm and retain excellent pure spectral extraction accuracy. In addition, the proposed architecture is easily scalable depending of the available resources and is more than three times faster than a recently developed FPGA implementation o...
FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis
IEEE Transactions on Geoscience and Remote Sensing, 2000
Hyperspectral remote sensing attempts to identify features in the surface of the Earth using sensors that generally provide large amounts of data. The data are usually collected by a satellite or an airborne instrument and sent to a ground station that processes it. The main bottleneck of this approach is the (often reduced) bandwidth connection between the satellite and the station, which drastically limits the information that can be sent and processed in real time. A possible way to overcome this problem is to include onboard computing resources able to preprocess the data, reducing its size by orders of magnitude. Reconfigurable field-programmable gate arrays (FPGAs) are a promising platform that allows hardware/software codesign and the potential to provide powerful onboard computing capability and flexibility at the same time. Since FPGAs can implement custom hardware solutions, they can reach very high performance levels. Moreover, using run-time reconfiguration, the functionality of the FPGA can be updated at run time as many times as needed to perform different computations. Hence, the FPGA can be reused for several applications reducing the number of computing resources needed. One of the most popular and widely used techniques for analyzing hyperspectral data is linear spectral unmixing, which relies on the identification of pure spectral signatures via a so-called endmember extraction algorithm. In this paper, we present the first FPGA design for N-FINDR, a widely used endmember extraction algorithm in the literature. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-4 XC4VFX60 FPGA (a model that is similar to radiation-hardened FPGAs certified for space operation) and tested using real hyperspectral data collected by NASA's Earth Observing-1 Hyperion (a satellite instrument) and the Airborne Visible Infra-Red Imaging Spectrometer over the Cuprite mining district in Nevada and the Jasper Ridge Biological Preserve in California. Experimental results demonstrate that our hardware version of the N-FINDR algorithm can significantly outperform an equivalent software version and is able to provide Manuscript accurate results in near real time, which makes our reconfigurable system appealing for onboard hyperspectral data processing.
Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing
Integration, the VLSI Journal, 2013
Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require advanced on-board processing algorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping of oil spills and chemical contamination, etc. One of the most widely used techniques for analyzing hyperspectral images is spectral unmixing, which allows for sub-pixel data characterization. This is particularly important since the available spatial resolution in hyperspectral images is typically of several meters, and therefore it is reasonable to assume that several spectrally pure substances (called endmembers in hyperspectral imaging terminology) can be found within each imaged pixel. In this paper we explore the role of hardware accelerators in hyperspectral remote sensing missions and further inter-compare two types of solutions: field programmable gate arrays (FPGAs) and graphics processing units (GPUs). A full spectral unmixing chain is implemented and tested in this work, using both types of accelerators, in the context of a real hyperspectral mapping application using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). The paper provides a thoughtful perspective on the potential and emerging challenges of applying these types of accelerators in hyperspectral remote sensing missions, indicating that the reconfigurability of FPGA systems (on the one hand) and the low cost of GPU systems (on the other) open many innovative perspectives toward fast on-board and on-the-ground processing of remotely sensed hyperspectral images.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It amounts the identification of pure spectral signatures (endmembers) in the data, and the estimation of the abundance of each endmember in each (possibly mixed) pixel. A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular and widely used techniques for this purpose is the HySime algorithm but, due to the complexity and high dimensionality of hyperspectral scenes, this technique is computational 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 HySime algorithm. Our system includes a direct memory access (DMA) module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASAs airborne visible infrared imaging spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World trade center (WTC) in New York. Experimental results demonstrate that our hardware version of the HySime algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.
2011
Endmember extraction is an important task for remotely sensed hyperspectral data exploitation. It comprises the identification of spectral signatures corresponding to macroscopically pure components in the scene, so that mixed pixels (resulting from limited spatial resolution, mixing phenomena happening at different scales, etc.) can be decomposed into combinations of pure component spectra weighted by an estimation of the proportion (abundance) of each endmember in the pixel. Over the last years, several algorithms have been proposed for automatic extraction of endmembers from hyperspectral images. These algorithms can be time-consuming (particularly for high-dimensional hyperspectral images). Parallel computing architectures have offered an attractive solution for fast endmember extraction from hyperspectral data sets, but these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power hardware components are essential to reduce mission payload, overcome downlink bandwidth limitations in the transmission of the hyperspectral data to ground stations on Earth, and obtain analysis results in (near) real-time. In this paper, we perform an inter-comparison of the hardware implementations of two widely used techniques for automatic endmember extraction from remotely sensed hyperspectral images: the pixel purity index (PPI) and the N-FINDR. The hardware versions have been developed in field programmable gate arrays (FPGAs). Our study reveals that these reconfigurable hardware devices can bridge the gap towards on-board processing of remotely sensed hyperspectral data and provide implementations that can significantly outperform the (optimized) equivalent software versions of the considered endmember extraction algorithms.
2006
Hyperspectral data compression is expected to play a crucial role in remote sensing applications. Most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop an FPGA-based data compression technique based on the concept of spectral unmixing. It has been implemented on a Xilinx Virtex-II FPGA formed by several millions of gates, and with high computational power and compact size, which make this reconfigurable device very appealing for onboard, real-time data processing.
2006
The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. The price paid for such a wealth of spectral information available from latest-generation sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models in remote sensing missions. This paper explores three HPC-based paradigms for efficient information extraction from remote sensing data using the Pixel Purity Index (PPI) algorithm (available from the popular Kodak's Research Systems ENVI software) as a case study for algorithm optimization. The three considered approaches are: 1) Commodity cluster-based parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGAbased hardware implementations. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC models to remote sensing problems.
FPGA Design of an Automatic Target Generation Process for Hyperspectral Image Analysis
2011 Ieee 17th International Conference on Parallel and Distributed Systems (Icpads), 2011
Onboard processing of remotely sensed hyperspectral data is a highly desirable goal in many applications. For this purpose, compact reconfigurable hardware modules such as field programmable gate arrays (FPGAs) are widely used. In this paper, we develop a new implementation of an automatic target generation process (ATGP) for hyperspectral images. Our implementation is based on a design methodology that starts from a high-level description in Matlab (or alternative C/C++) and obtains a register transfer level (RTL) description that can be ported to FPGAs. In order to validate our new implementation, we develop a quantitative and comparative study using two different FPGA architectures: Xilinx Virtex-5 and Altera Stratix-III Altera. Experimental results have been obtained in the context of a real application focused on the detection of mineral components over the Cuprite mining district (Nevada), using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experimental results indicate that the proposed implementation can achieve peak frequency designs above 200MHz in the considered FPGAs, in addition to satisfactory results in terms of target detection accuracy and parallel performance. This represents a step forward towards the design of real-time onboard implementations of hyperspectral image analysis algorithms.
FPGA Design of the N-FINDR Algorithm for Spaceborne Hyperspectral Missions
2011
Hyperspectral imaging is a new technique in remote sensing which generates hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Each pixel collected by a hyperspectral remote sensing instrument is in fact a spectral signature of the underlying materials. Many algorithms attempt to find pure spectral signatures in the image data, called endmembers, and use this information to identify the underlying materials within each pixel. The N-FINDR algorithm is one of the most popular and widely used, despite its high computational complexity when applied to high-dimensional images. However, implementations of this algorithm on reconfigurable hardware are not yet available in the literature, despite the potential advantages that can be gained from processing the hyperspectral images onboard the sensor using specialized hardware devices. In this paper, we present a field programmable gate array design of the algorithm, which has been implemented ...