# FPGA Implementation of the Pixel Purity Index Algorithm for Remotely Sensed Hyperspectral Image Analysis

- Carlos González
^{1}Email author, - Javier Resano
^{2}, - Daniel Mozos
^{1}, - Antonio Plaza
^{3}and - David Valencia
^{3}

**2010**:969806

https://doi.org/10.1155/2010/969806

© Carlos González et al. 2010

**Received: **17 February 2010

**Accepted: **28 May 2010

**Published: **14 June 2010

## Abstract

Hyperspectral imaging is a new emerging technology in remote sensing which generates hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Over the last years, many algorithms have been developed with the purpose of finding endmembers, assumed to be pure spectral signatures in remotely sensed hyperspectral data sets. One of the most popular techniques has been the pixel purity index (PPI). This algorithm is very time-consuming. The reconfigurability, compact size, and high computational power of Field programmable gate arrays (FPGAs) make them particularly attractive for exploitation in remote sensing applications with (near) real-time requirements. In this paper, we present an FPGA design for implementation of the PPI algorithm. Our systolic array design includes a DMA and implements a prefetching technique to reduce the penalties due to the I/O communications. We have also included a hardware module for random number generation. The proposed method has been tested using real hyperspectral data collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite mining district in Nevada. Experimental results reveal that the proposed hardware system is easily scalable and able to provide accurate results with compact size in (near) real-time, which make our reconfigurable system appealing for on-board hyperspectral data processing.

## Keywords

## 1. Introduction

*spectral signature*or

*fingerprint*of the underlying materials within the pixel (see Figure 1).

Several analytical tools have been developed for hyperspectral data processing in recent years, covering topics like dimensionality reduction, classification, data compression, or spectral mixture analysis [3]. The underlying assumption governing clustering and classification techniques is that each pixel vector comprises the response of a single underlying material. However, if the spatial resolution of the sensor is not high enough to separate different materials, these can jointly occupy a single pixel and the resulting spectral measurement will be a *mixed* pixel, that is, a composite of the individual pure spectra. For instance, in Figure 1 it is likely that the pixel labeled as "vegetation" is actually a mixture of vegetation and soil, or of different types of vegetation canopies.

To deal with this problem, linear spectral mixture analysis techniques [4] first identify a collection of spectrally pure constituent spectra, called *endmembers* in hyperspectral analysis terminology [5] and then express the measured spectrum of each mixed pixel as a linear combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember present in the pixel. In fact, spectral mixture analysis has been an alluring exploitation goal since the earliest days of hyperspectral imaging [4]. No matter the spatial resolution, in natural environments the spectral signature for a nominal pixel is invariably a mixture of the signatures of the various materials found within the spatial extent of the ground instantaneous field view of the sensor. In hyperspectral imagery, the number of spectral bands usually exceeds the number of pure spectral components and the unmixing problem is cast in terms of an overdetermined system of equations in which given the correct set of endmembers allows determination of the actual endmember abundance fractions through a numerical inversion process.

where designates the th pure spectral component (endmember) residing in the pixel, is a scalar value designating the fractional abundance of the endmember at the pixel , is the total number of endmembers, and is a noise vector. The solution of the linear spectral mixture problem described in (1) relies on the correct determination of a set of endmembers. It is such derivation and validation of the correct suite of endmembers that has remained a challenging goal for the past years (not only in terms of adequate spectral signature extraction, but also in terms of computational complexity [6]).

- (1)
First, a pixel purity score is calculated for each pixel vector in the input hyperspectral image cube by generating random, -dimensional vectors, called

*skewers*. - (2)
Then, each pixel vector in the input data is projected onto the entire set of skewers , and the pixels falling at the extremes of each skewer are tallied (see Figure 2). After many repeated projections to different random skewers, those pixels which are repeatedly selected during the process are identified and placed on a list of endmember candidates.

- (3)
The potential endmember spectra are then loaded into an interactive tool (such as ENVI's -dimensional visualizer, available as a built-in companion piece in ENVI software) and rotated until a desired number of endmembers are visually identified as extreme pixels in the data cloud.

The PPI algorithm suffers from several limitations [10]. First and foremost, the algorithm is sensitive to parameter , that is, the number of skewers. Since the skewers are randomly generated, a large number of skewer projections are generally required in order to arrive to satisfactory endmember sets in terms of signature purity. The authors recommend using as many random skewers as possible in order to obtain optimal results [7]. As a result, the PPI can only guarantee to produce optimal results asymptotically and its computational complexity is very high, thus requiring efficient implementations. Another shortcoming of the PPI is the fact that an interactive tool is needed to perform the final endmember selection. An alternative is to retain the pixels that have been selected above a predefined threshold and then automatically remove spectrally redundant endmembers [10]. This is generally treated as a postprocessing stage external to the algorithm.

An exciting new development in the field of specialized commodity computing for accelerating computationally intensive algorithms is the emergence of hardware devices such as field programmable gate arrays (FPGAs) [11–13], which can bridge the gap towards on-board and real-time analysis of remote sensing data [14, 15]. FPGAs are now fully reconfigurable [16, 17], a technological feature that, in our application context, allows a control station on Earth to adaptively select a data processing algorithm (out of a pool of available algorithms implemented on the FPGA) to be applied on board the sensor. The ever-growing computational demands of hyperspectral imaging applications can fully benefit from compact, reconfigurable hardware components and take advantage of the small size and relatively low cost of these units.

In this paper, we develop an FPGA-based hardware version of the PPI algorithm. The proposed implementation is aimed at enhancing code reusability and efficient implementation in FPGA devices through the utilization of systolic array design. One of the main advantages of systolic array-based implementations is that they are able to provide a systematic procedure for system design that allows for the derivation of a well-defined processing element-based structure and an interconnection pattern which can then be easily ported to real hardware configurations. The remainder of the paper is organized as follows. Section 2 discusses the role of reconfigurable hardware in remote sensing missions. Section 3 describes our implementation of the PPI algorithm. Section 4 describes its parallel implementation on a Xilinx Virtex-II PRO xc2vp30 FPGA. Section 5 provides an experimental assessment of both endmember extraction accuracy and parallel processing performance of the proposed FPGA-based algorithm, using a well-known hyperspectral data set (with quality ground-truth) collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer (AVIRIS) [2] over the Cuprite mining district in Nevada. Finally, Section 6 concludes with some remarks and hints at plausible future research lines.

## 2. The Role of Reconfigurable Hardware in Remote Sensing Missions

The trend in remote sensing missions has always been towards using hardware devices with smaller size, lower cost, more flexibility, and higher computational power [18, 19]. On-board processing, as a solution, allows for a good reutilization of expensive hardware resources. Instead of storing and forwarding all captured images, remote sensing data interpretation can be performed on orbit prior to downlink, resulting in a significant reduction of communication bandwidth as well as simpler and faster subsequent computations to be performed at ground stations. In this regard, FPGAs combine the flexibility of traditional microprocessors with the power and performance of application-specific integrated circuits (ASICs). Therefore, FPGAs are a promising candidate for on-board remote sensing data processing.

Recently, FPGAs have become a viable target technology for implementation of remotely sensed hyperspectral imaging algorithms [20]. These computing systems combine the flexibility of general purpose processors with the speed of application-specific processors. Reconfigurable hardware offers the necessary flexibility and performance with reduced energy consumption compared to other high performance processors. By mapping functionality to FPGAs, the computer designer can optimize the hardware for a specific application resulting in acceleration rates of several orders of magnitude over general-purpose computers. In addition, these devices are characterized by lower form/wrap factors compared to parallel platforms and by higher flexibility than ASIC solutions. Reconfigurable computing technology further allows new hardware circuits to be uploaded via a radio link for physical upgrade or repair [21].

Moreover, satellite-based remote sensing instruments can only include chips that had been certified for space conditions. This is because space-based systems must operate in an environment in which radiation effects have an adverse impact on integrated circuit operation [22]. Ionizing radiation can cause soft-errors in the static cells used to hold the configuration data. This will affect the circuit functionality and can cause system failure. So it requires special FPGAs that provide on-chip reconfiguration error-detection and/or correction circuitry. High-speed, radiation-hardened FPGA chips with million gate densities have recently emerged that can support the high throughput requirements for the remote sensing applications. Radiation-hardened FPGAs are in great demand for military and space applications. For instance, industrial partners such as Actel Corporation (http://www.actel.com/) or Xilinx (http://www.xilinx.com/) have been producing radiation-tolerant antifuse FPGAs for several years for high-reliability space-flight systems. Actel FPGAs have been on board more than 100 launches and Xilinx FPGAs have been used in more than 50 missions [22]. In this work, we use a Xilinx Virtex-II PRO xc2vp30 FPGA as a baseline architecture because it is similar to other FPGAs [23] that have been certified by several international agencies for remote sensing applications. They are based on the same architecture so we could immediately implement our design on them.

## 3. The Pixel Purity Index (PPI) Algorithm

Since the details of the specific steps to implement ENVI's PPI are not available in the literature, the PPI algorithm described below is only based on the limited published results and our own interpretation [10]. Nevertheless, except a final manual supervision step (included in ENVI's PPI) which is replaced by step 4, both our approximation and the PPI in ENVI 4.0 produce very similar results. The inputs to the algorithm are a hyperspectral data cube **F** with
dimensions; the number of random skewers to be generated during the process, *K*; and a cut-off threshold value,
, used to select as final endmembers only those pixels that have been selected as extreme pixels at least
times throughout the PPI process.

- (1)
- (2)
*Extreme projections*. For each , , all pixel vectors in the original data set**F**are projected onto via dot products of to find sample vectors at its extreme (maximum and minimum) projections, thus forming an extrema set for which is denoted by . Despite the fact that different skewers generate different extrema sets, it is very likely that some sample vectors may appear in more than one extrema set. To account for this, we define an indicator function of a set**F**, denoted by , to denote membership of an element**x**to that particular set as follows: - (3)
- (4)
*Endmember selection*. Find the pixel vectors with scores of which are above and label them as spectral endmembers. An optional postprocessing (not implemented in this work) based on removing potentially redundant endmembers may be also applied.

The most time-consuming stage of the PPI algorithm is stage 2 (extreme projections). For example, running this stage on a hyperspectral image with pixels (the standard number of pixels produced by NASA's AVIRIS instrument in a single frame, each with 224 spectral bands) using skewers requires the calculation of more than multiplication/accumulation (MAC) operations, that is, a few hours of nonstop computation on a 500 MHz microprocessor with 256 Mbytes SDRAM [24, 25]. In [20], another example is reported in which the PPI algorithm available in ENVI 4.0 version took more than 50 minutes of computation to project every data sample vector of a hyperspectral image with the same size reported above onto skewers in a PC with AMD Athlon 2.6 GHz processor and 512 MB of RAM.

Fortunately, the PPI algorithm is well suited for parallel implementation. The computation of skewer projections is independent and can be performed simultaneously, leading to many ways of parallelization. In [24, 25], two parallel architectures for implementation of the PPI are proposed. Both are based on a 2D processor array tightly connected to a few memory banks. A speedup of 80 is obtained through an FPGA implementation on the Wildforce board (4 Xilinx XC4036EX plus 4 memory banks of 512 Kbytes) [26]. As a matter of fact, this design is tailored to the Wildforce board and it cannot be reused for another board without huge modifications. In [10], a fast iterative PPI (FPPI) is introduced. The Matlab-based software implementation of the FPPI algorithm was more than 24 times faster than the ENVI's PPI algorithm in the same computing environment, while the FPGA-based implementation showed a significant increase in performance with regards to the two considered software versions due to the low-level hardware implementation. Although these works have demonstrated the efficiency of a hardware implementation on a reconfigurable board, these solutions are not scalable.

The FPGA implementation that we present in the following section aims at overcoming these drawbacks. First, our architecture specification can be easily adapted to different platforms. Second, our proposed architecture is scalable depending on the amount of available resources because the required resources grow proportionally with the number of skewers and the clock cycle remains constant.

## 4. FPGA Implementation

### 4.1. Parallel Design Strategies for the PPI Algorithm

Taking in mind the above rationale, in this work we have selected the parallelization strategy based on skewers. Apart from the aforementioned advantages with regard to other possible strategies, another reason for this selection is that the parallelization strategy based on skewers fits very well how the image data reaches the system. In our case, our goal is to make an on-line processing of the hyperspectral images bearing in mind that hyperspectral sensors capture the image data in a pixel by pixel fashion. Therefore, parallelization by skewers is the one that best fits the data entry mechanism since each pixel can be processed immediately as collected. Specifically, our hardware system should be able to compute
dot products at the same time against the same pixel
, where
is the number of skewers. In such a system, the *extreme projections* step of the PPI (the most time-consuming one in the PPI process) can be simply written as described in Algorithm 1.

**Algorithm 1:** Parallel implementation of *extreme projections* step.

**for** (
;
;
++)
//
denotes the number of pixels

**par** (
;
;
++)
//
denotes the number of skewers

dp =dot_product(pixels ,skewers );

if (dp Min ) Min =dp ; Reg_Min = ;

if (dp Max ) Max =dp ; Reg_Max = ;

The **par** loop in Algorithm 1 expresses that
dot products are first performed in parallel, then
Min and Max operations are also computed in parallel. Now, if we suppose that we cannot simultaneously compute
dot products but only a fraction
, where
is the number of available processing units in the underlying parallel platform, then the *extreme projections* step can be split into
passes, each performing
dot products, as indicated in Algorithm 2. From an architectural point of view, each processor receives successively the
pixels, computes
dot-products, and keeps in memory the Min and the Max dot products. In this scheme, each processor holds a different skewer which must be input before each new pass.

**Algorithm 2:** Parallel implementation of *extreme projections* step (rewritten to be split into
algorithm iterations).

**for** (
;
;
++)
//
is the number of algorithm iterations

; // denotes the number of skewers

**for** (
;
;
++)
//
denotes the number of pixels

dp[ ]=dot_product(pixels[ ],skewers[ ]);

if (dp[ ] Min[ ]) Min[ ]=dp[ ]; Reg_Min[ ]= ;

if (dp[ ] Max[ ]) Max[ ]=dp[ ]; Reg_Max[ ]= ;

### 4.2. Hardware Implementation

Taking into account that the latency of an addition or a subtraction is just one clock cycle, then the calculation of a dot product requires clock cycles. In each cycle, the processor sequentially receives the data of a pixel and accumulates the result, adding or subtracting, depending on the skewer component. The additional clock cycle is required for the comparison with a max and a min value and the pixel updating. We have evaluated different options to remove the last clock cycle, but finally we have decided to keep it. One option was to update the min and max indexes in parallel with the computation of the next dot product, but it requires a more complex hardware mechanism (at least two more registers) and makes this solution worse globally because we can synthesize less systolic processors on the FPGA. We can also update the pixel during the last clock cycle of each systolic cycle, but it increases the critical path and increases the clock frequency. Hence, when is a large number (as in the case of hyperspectral images), we obtain higher computation times.

- (i)
Firstly, to initialize the random generation module, the PowerPC generates two seeds of bits (where is the number of skewers) and writes them to the Write FIFO.

- (ii)
Afterwards, the control unit reads these seeds and sends them to the random generation module where they are stored. Hence, the random generation module can provide the systolic array with one bit for each skewer every clock cycle as we have described in this section.

- (iii)
After the PowerPC has written the two seeds, it sends an order to the DMA to start copying a piece of the image from the DDR2 SDRAM to the Write FIFO. As mentioned before, the main bottleneck in this kind of system is frequently the data input which is addressed in our implementation by the incorporation of a DMA that eliminates most I/O overheads. Moreover, the PowerPC monitors the input FIFO and sends a new order to the DMA every time that it detects that the Write FIFO is half empty. This time, the DMA will bring a piece of the image that occupies half of the Write FIFO total capacity.

- (iv)
When the data of the first pixel have been written in the Write FIFO, the systolic array and the random generation module start working. Every clock cycle, a new pixel is read by the control unit and sent to the systolic array. In parallel, the th component of each skewer also is sent to the systolic array by the random generation module.

- (v)
During clock cycles, data of a pixel are accumulated positively or negatively depending of the skewer component. In the next clock cycle, the Min/Max unit updates the pixel and the random generation module restores the original two seeds, concluding the systolic cycle. In order to process the hyperspectral image, we need as many systolic cycles as pixels in the image. When the entire image is processed, the control unit writes the endmembers to the Read FIFO.

- (vi)
Finally, the Transmitter extracts the endmembers from the Read FIFO and sends them via an RS232 port.

- (vii)
These steps are repeated several times depending on the number of skewers we can parallelize and the number of skewers we want to evaluate.

## 5. Experimental Results

### 5.1. FPGA Architecture

The hardware architecture described in Section 4 has been implemented using VHDL language for the specification of the systolic array. Further, we have used the Xilinx ISE environment and the Embedded Development Kit (EDK) environment (http://www.xilinx.com/ise/embedded/edk_pstudio.html) to specify the complete system. The full system has been implemented on an XUPV2P board, a low-cost reconfigurable board with a single Virtex-II PRO xc2vp30 FPGA component, a DDR SDRAM DIMM slot which holds up to 2 GBytes, an RS232 port, and some additional components not used by our implementation.

### 5.2. Hyperspectral Data

### 5.3. Endmember Extraction Accuracy Evaluation

Spectral angle-based similarity scores between the endmembers extracted by different implementations of PPI and the selected USGS reference signatures.

USGS | ENVI | PPI | Our FPGA-based |
---|---|---|---|

mineral | software | approximation | PPI |

Alunite | 0.084 | 0.084 | 0.084 |

Buddingtonite | 0.071 | 0.068 | 0.068 |

Calcite | 0.089 | 0.089 | 0.089 |

Kaolinite | 0.136 | 0.132 | 0.132 |

Muscovite | 0.092 | 0.081 | 0.081 |

In order to display the results in a more effective manner, we only report the SAD score associated to the most similar spectral endmember with regards to its corresponding USGS signature. It is important to emphasize that smaller SAD values indicate higher spectral similarity. As shown by Table 1, the two considered implementations did not produce exactly the same results as those obtained by the original PPI algorithm implemented in Research Systems ENVI 4.0. This is because ENVI's PPI implementation includes a manual supervision procedure to select the final endmembers and, hence, it is user dependent. In our experiments with the -dimensional visualization tool available in ENVI, we made sure to perform many interactive rotations in order to select the best possible endmembers. In any event, both our PPI approximation in Section 3 and the FPGA implementation in Section 4 produced very similar results to those found by ENVI's PPI, but in a fully automatic fashion.

### 5.4. Parallel Performance Evaluation

Summary of resource utilization for the FPGA-based implementation of the PPI algorithm.

Component | Number of skewers | Number of slice flip flops | Number of 4 input LUTs | Number of slices | Percentage of total | Maximum operation frequency (MHz) |
---|---|---|---|---|---|---|

Systolic Array | 20 | 2240 | 3865 | 2085 | 15.22 | 187 |

40 | 4480 | 7728 | 4170 | 30.44 | 187 | |

60 | 6720 | 11591 | 6254 | 45.66 | 187 | |

80 | 8960 | 15454 | 8339 | 60.88 | 187 | |

100 | 11200 | 19317 | 10423 | 76.1 | 187 | |

Random Generation Module | 20 | 40 | 120 | 58 | 0 | 664 |

40 | 80 | 240 | 115 | 0.42 | 664 | |

60 | 120 | 360 | 173 | 0.84 | 664 | |

80 | 160 | 480 | 230 | 1.68 | 664 | |

100 | 200 | 600 | 288 | 2.1 | 664 | |

RS232 Transmitter | — | 69 | 128 | 71 | 0.52 | 208 |

DMA Controller | — | 170 | 531 | 367 | 2.68 | 102 |

Frequently communications are the main bottleneck of a parallel system. Hence, we have paid special attention to this problem. In previous designs [10, 20], the random generation module was situated in an external processor. Hence, frequent communications were demanded. One of the improvements of our system is that we have developed a hardware random generation module based on the design proposed in [29]. This approach significantly reduces the I/O communications. Moreover, to further reduce the I/O overheads we have included DMA and we have applied a prefetching approach in order to hide the communication latency. Basically, while the systolic array is processing a set of data, the DMA is fetching the following set and storing it in the Write FIFO. Having in mind the proposed optimization concerning the use of available resources, it is important to find a balance between the number of DMA operations and the capacity of the destination FIFO. In other words, we need to fit enough information in the Write FIFO so that the systolic array never needs to stop. In addition, the greater the Write FIFO capacity, the fewer DMA operations will be required. We have evaluated several Write FIFO sizes and identified that, for 1024 positions or more, there are no penalties due to reading of the input data. To demonstrate the advantages of using a DMA, we have developed another version in which the image data are read from memory and written to the Write FIFO by the PowerPC instead of the DMA. In this version, the processing time was increased more than an order of magnitude (340 seconds) so we can conclude that the resources used for the DMA (621 slices) are well spent.

Comparison of different implementations of the PPI algorithm.

Algorithm | PPI Approximation | FPGA implementation in [20] | Proposed FPGA implementation |
---|---|---|---|

Processing time (seconds) | 3068 | 62 | 31 |

## 6. Conclusions and Future Research Lines

On-board data processing of hyperspectral imagery has been a long-awaited goal by the remote sensing community. The number of applications requiring a response in real-time has been growing exponentially in recent years. Current sensor design practices could greatly benefit from the inclusion of specialized processing modules, such as FPGAs, which can be easily mounted or embedded in the sensor due to its compact size. In this paper, we have described an FPGA implementation of an advanced algorithm for information extraction from remotely sensed hyperspectral scenes. The algorithm selected for demonstration has been the Pixel Purity Index (PPI), one of the most well-known approaches for hyperspectral data analysis in the remote sensing community. Our experimental results, conducted on a Xilinx Virtex-II PRO xc2vp30 FPGA (a platform with the same architecture and similar area than radiation-hardened FPGAs that have been certified by international remote sensing agencies and are commonly used in airborne and spaceborne Earth Observation platforms), demonstrate that our hardware implementation makes appropriate use of computing resources in the considered architecture. Further, our proposed hardware version of the PPI algorithm can significantly outperform (in terms of computation time) the original (semisupervised) version of the algorithm, available in commercial software, a (fully automatic) approximation of the algorithm, and a recently developed FPGA implementation developed for a Xilinx Virtex-II XC2V6000-6 FPGA. Another interesting feature of our implementation is that it can be easily scaled to fit on larger FPGAs.

The reconfigurability of FPGA systems opens many innovative perspectives from the remote sensing application point of view, ranging from the appealing possibility of being able to adaptively select the data processing algorithm to be applied on board, out of a pool of available algorithms, from a control station on Earth immediately after the data is collected by the sensor, to the possibility of providing a real-time response in remote sensing applications with real-time requirements. As future work, we are investigating FPGA implementations of other endmember extraction algorithms based on different concepts and evaluating other specialized hardware platforms for on-board hyperspectral data exploitation, such as commodity graphics processing units (GPUs).

## Authors’ Affiliations

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