A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs

Zhao, Chunhui; Li, Jiawei; Meng, Meiling; Yao, Xin

doi:10.3390/s17030441

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…In [9], a weighted spatial-spectral kernel detection is achieved by reconstructing the central pixel using the spatial neighborhood information. Efficient implementation is obtained by using GPU.…”

Section: Related Workmentioning

confidence: 99%

“…More in detail, GPU seems to be used especially to support on-ground operations such as: pixel detection or classification and spectral signature extraction. In [ 9 ], a weighted spatial-spectral kernel detection is achieved by reconstructing the central pixel using the spatial neighborhood information. Efficient implementation is obtained by using GPU.…”

Section: Related Workmentioning

confidence: 99%

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ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

Giordano

Guccione

2017

Sensors

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In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA’s CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target’s related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

Giordano

Guccione

2017

Sensors

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“…Although the previous work has improved the anomaly detection performance to a certain extent [32][33][34], the detection accuracy and detection efficiency still warrant further improvement. Therefore, to avoid this problem, an anomaly detection algorithm supporting vector data description (SVDD) [35] was proposed.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Spatial Density Background Purification

Liao

et al. 2019

Remote Sensing

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In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.

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Salient object detection on hyperspectral images in wireless network using CNN and saliency optimization

Huang

Zhang

et al. 2021

Ad Hoc Networks

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A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs

Cited by 6 publications

References 24 publications

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

Hyperspectral Anomaly Detection via Spatial Density Background Purification

Salient object detection on hyperspectral images in wireless network using CNN and saliency optimization

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