Applications of Grid Pattern Matching to the Detection of Buried Landmines

Thomas, Alan M.; Cathcart, J. Michael

doi:10.1109/tgrs.2010.2046740

Cited by 14 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, pattern parameters are extracted and used to form a pattern projection image. Finally, a pattern-based false alarm reduction is performed [43]. Using this process, higher probability of detection at lower false alarm rate is obtained.…”

Section: Night Vision and Electronics Systems Directorate (Nvesd)mentioning

confidence: 99%

A survey of landmine detection using hyperspectral imaging

Makki

Younès

Francis

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

136

View full text Add to dashboard Cite

Section: Night Vision and Electronics Systems Directorate (Nvesd)mentioning

confidence: 99%

A survey of landmine detection using hyperspectral imaging

Makki

Younès

Francis

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

136

View full text Add to dashboard Cite

“…Currently, spectral imaging is being used in many different areas of scientific research and engineering applications. Satellite-based remote sensing [3], agriculture [4], the defense industry [5], medical diagnostics [6], and food inspection [7] are just a few examples of situations in which the use of spectral imaging is very popular. Diffusely reflected EM waves contain specific object signatures depending upon the temperature and material of the reflecting surfaces in the scene [8].…”

Section: Introductionmentioning

confidence: 99%

“…T = {1, 2,3,4,5,6,7,8,9,10,15,20,30, 40, 50, 60, 70, 80, 90, 120, 160, 200, 250, 300, 350, 400, 466} ms. Some of these images are shown in Figure9.…”

mentioning

confidence: 99%

Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging

Shaikh

Jaferzadeh²,

Thörnberg³

2022

Sensors

View full text Add to dashboard Cite

In this work, a multi-exposure method is proposed to increase the dynamic range (DR) of hyperspectral imaging using an InGaAs-based short-wave infrared (SWIR) hyperspectral line camera. Spectral signatures of materials were captured for scenarios in which the DR of a scene was greater than the DR of a line camera. To demonstrate the problem and test the proposed multi-exposure method, plastic detection in food waste and polymer sorting were chosen as the test application cases. The DR of the hyperspectral camera and the test samples were calculated experimentally. A multi-exposure method is proposed to create high-dynamic-range (HDR) images of food waste and plastic samples. Using the proposed method, the DR of SWIR imaging was increased from 43 dB to 73 dB, with the lowest allowable signal-to-noise ratio (SNR) set to 20 dB. Principal Component Analysis (PCA) was performed on both HDR and non-HDR image data from each test case to prepare the training and testing data sets. Finally, two support vector machine (SVM) classifiers were trained for each test case to compare the classification performance of the proposed multi-exposure HDR method against the single-exposure non-HDR method. The HDR method was found to outperform the non-HDR method in both test cases, with the classification accuracies of 98% and 90% respectively, for the food waste classification, and with 95% and 35% for the polymer classification.

show abstract

“…With current trends and advancements in HI sensors, capturing and gathering HI data has become more affordable and accessible, making it useful in a variety of real-world applications. Some real-life applications include agriculture (Liu et al, 2004;Ben-Dor et al, 2002), food-grade assessment (Gowen et al, 2007;Feng and Sun, 2012), aquatic resources (Adam et al, 2010;Govender et al, 2007), armed force and country security surveillance (Goldberg et al, 2003;Thomas and Cathcart, 2010), medical imaging (Afromowitz et al, 1988;Carrasco et al, 2003), geology (Clark and Swayze, 1995), document imaging and analysis (Padoan et al, 2008;Melessanaki et al, 2001), etc.. Hyperspectral image is usually associated with its spatial dimensions (A x and A y ) and spectral dimension (A λ ). A λ helps in discovering the significant variations of reflectance between image pixels which change with wavelength (Khan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

HybridMSSN: A Deep Learning Hyperspectral Image Classification Model

Ladi

PANDA²,

Dash

et al. 2022

Preprint

View full text Add to dashboard Cite

With the goal of obtaining accuracy and optimum computation, Hyperspectral image (HI) classification models have been developed for best use in a variety of applications. Because HI contains a lot of spatial and spectral information, classification using merely a Convolutional Neural Network (CNN) isn't always enough. This paper solves these concerns by proposing a new Hybrid Multi-Scale Spinal Net (HybridMSSN). The HI is subjected to Principal Component Analysis (PCA) in order to isolate the most useful spectral bands and eliminate the superfluous ones. For feature learning and classification, the model uses multiscale CNNs and a Spinal Fully Connected Network (SFCN). Three 3D-CNNs are used to extract spectral and spatial features from the 3-dimensional HI, followed by a 2D-CNN for spatial feature learning. The model uses SFCNs to optimize computation, reducing the number of multiplications in the activation function of neural networks and resulting in a faster response. The multi-scale features acquired from the different 3D Convolution layers are flattened and transferred to independent SFCNs after Max Pooling, Dropout, and Batch Normalization. Similarly, features collected from the 2D convolution layer and Dropout are flattened and supplied to the SFCN. For categorization, the SFCNs' outputs are combined. Despite limited training samples, the existence of noise, and an imbalance of class concerns, investigations conducted on three standard datasets revealed significant accuracy gains when compared to four state-of-the-art models.

show abstract

Applications of Grid Pattern Matching to the Detection of Buried Landmines

Cited by 14 publications

References 20 publications

A survey of landmine detection using hyperspectral imaging

A survey of landmine detection using hyperspectral imaging

Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging

HybridMSSN: A Deep Learning Hyperspectral Image Classification Model

Contact Info

Product

Resources

About