Ground Control Point Automatic Extraction for Spaceborne Georeferencing Based on FPGA

Liu, Dequan; Zhou, Guoqing; Zhang, Dianjun; Zhou, Xiang; Li, Chenyang

doi:10.1109/jstars.2020.2998838

Cited by 8 publications

(9 citation statements)

References 74 publications

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“…The resulting frame rate of the proposed double E-SIFT with FAP feature matching is 181 fps, which is faster than most of the approaches. Although reference [27] implementing SURF detector and BRIEF descriptor has the fastest frame rate [27], the required hardware resource is much higher than the proposed method in this paper. The quantities of LEs in [14] and [28] are close to the proposed stereo vision system, but the resulting frame rates are only about 40 fps.…”

Section: Performance Of Proposed E-siftmentioning

confidence: 93%

“…The determinant of H3×3 can be obtained by repeatedly using the first row of the adjugate matrix combined with elements in the first column of the determinant to reduce the hardware cost. Thus, Equation (27) can be rewritten as:…”

Section: High Contrast Detectionmentioning

confidence: 99%

“…Figure27. The same stereo visions as (a) Figure26a, and (b) Figure26bare aligned vertically to verify the accuracy.…”

mentioning

confidence: 99%

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FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision

et al. 2021

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In this paper, we propose an FPGA-based enhanced-SIFT with feature matching for stereo vision. Gaussian blur and difference of Gaussian pyramids are realized in parallel to accelerate the processing time required for multiple convolutions. As for the feature descriptor, a simple triangular identification approach with a look-up table is proposed to efficiently determine the direction and gradient of the feature points. Thus, the dimension of the feature descriptor in this paper is reduced by half compared to conventional approaches. As far as feature detection is concerned, the condition for high-contrast detection is simplified by moderately changing a threshold value, which also benefits the reduction of the resulting hardware in realization. The proposed enhanced-SIFT not only accelerates the operational speed but also reduces the hardware cost. The experiment results show that the proposed enhanced-SIFT reaches a frame rate of 205 fps for 640 × 480 images. Integrated with two enhanced-SIFT, a finite-area parallel checking is also proposed without the aid of external memory to improve the efficiency of feature matching. The resulting frame rate by the proposed stereo vision matching can be as high as 181 fps with good matching accuracy as demonstrated in the experimental results.

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Section: Performance Of Proposed E-siftmentioning

confidence: 93%

Section: High Contrast Detectionmentioning

confidence: 99%

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FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision

et al. 2021

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“…This method is inefficient and places significant pressure on the ground system. With advancements in various intelligent on-orbit processing techniques [6,7], it is now possible to pre-store high-precision reference data on-orbit to achieve real-time geometric processing of remote sensing images. Common reference data comprise high-resolution reference images and control point databases.…”

Section: Introductionmentioning

confidence: 99%

Lightweight and Stable Multi-Feature Databases for Efficient Geometric Localization of Remote Sensing Images

Zhao,

Wang,

You

2024

Remote Sensing

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The surge in remote sensing satellites and diverse imaging modes poses substantial challenges for ground systems. Swift and high-precision geolocation is the foundational requirement for subsequent remote sensing image applications. Breakthroughs in intelligent on-orbit processing now enable on-orbit geometric processing. In the absence of control data on board, a recent trend is to introduce reference data onto satellites. However, the pre-storage of traditional reference images or control point databases presents a significant challenge to the limited on-board data storage capacity. Therefore, oriented to the demand for control information acquisition during on-orbit geometry processing, we propose the construction of lightweight and stable feature databases. Initially, stable feature classes are obtained through iterative matching filtering, followed by re-extracting feature descriptors for each stable feature point location on the training images. Subsequently, the descriptors of each point location are clustered and fused using affinity propagation (AP) to eliminate redundancy. Finally, LDAHash is utilized to quantize floating-point descriptors into binary descriptors, further reducing the storage space. In our experiments, we utilize a variety of feature algorithms to assess the generality of our proposed method, thus extending the scope of the feature database and its applicability to various scenarios. This work plays a crucial role in advancing the technology of on-orbit geometry processing for remote sensing satellites.

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“…A first strategy to discriminate textures is based on the processing of the tactile image taken by a high resolution tactile sensor [4] [5]. This approach can take advantage of resources for feature extraction in image processing [6], and also of their efficient i mplementation o n e mbedded systems [7], and specifically on FPGAs [8]. Nevertheless, there is still a significant need of computational means, power consumption, This work was submitted on 10.04.2023.…”

Section: Introductionmentioning

confidence: 99%

Texture Detection With Feature Extraction on Embedded FPGA

Lora-Rivera¹,

Oballe-Peinado²,

Vidal‐Verdú³

2023

IEEE Sensors J.

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A feature extraction algorithm for texture detection oriented to its implementation on embedded electronics based on a Field-Programmable Gate Array (FPGA) is proposed in this paper. Local pre-processing with smart tactile sensors can help to improve dexterity in artificial hands. Simplicity is the goal in order to achieve a hardware-friendly strategy that can be replicated and integrated with another circuitry. This is interesting, considering that tactile sensors are arrays and FPGAs are capable of parallel execution. The proposal was tested with a custom smart tactile sensor mounted on a Cartesian robot to explore different textures. A comparison with a common feature extraction approach based on the fast Fourier Transform (FFT) computation was also made. In addition, the whole procedure is implemented on a System on Chip (SoC) with the feature extraction on the embedded FPGA and a k-means classifier on an ARM core. The proposed algorithm obtains the spatial frequency components of the tactile signal but not their power. Therefore, some information is lost with respect to that provided by the FFT. Nevertheless, an 89.17% accuracy of the proposed algorithm is obtained versus 91.4% with the FFT when twelve different textures are considered, including complex and fabric textures. There is a noticeable saving in power and hardware resources. In addition, since the size of the feature vector is much smaller, data traffic and memory usage is much lower, and the classifier can be simpler.

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Ground Control Point Automatic Extraction for Spaceborne Georeferencing Based on FPGA

Cited by 8 publications

References 74 publications

FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision

FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision

Lightweight and Stable Multi-Feature Databases for Efficient Geometric Localization of Remote Sensing Images

Texture Detection With Feature Extraction on Embedded FPGA

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