Identification of military vehicles in hyper spectral imagery through spatio-spectral filtering

Prashnani, M.; Chekuri, Ravi Shankar

doi:10.1109/iciip.2013.6707648

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The convolutional layer structure employs the same structure as YOLOv5, i.e., Convolution2D + Batch Normalization + Leaky Relu. Darknet 53 is used in YOLOV5 and in enhanced YOLOv5 model Darknet53+EfficientNet 227 [21] layers are used.…”

Section: B Methodologymentioning

confidence: 99%

Onboard Processing of Drone Imagery for Military Vehicles Classification Using Enhanced YOLOv5

Raj,

Sahithi

et al. 2023

JAIT

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Recently, drones are used in all fields. The video captured by this drone is sent to the terminal for analysis. In terms of speed, performance, and latency, it would be an advantage if the analysis of the image or video is done onboard, the drone, and the result is sent to terminal, this is called onboard processing. For faster recognition speed and higher frame rate, YOLOv5 is used for image detection along with EfficientNet-b0 for classification and de-blurring with DeblurGan v2. A custom dataset of 6999 military vehicle images is created and annotated. This model is loaded in Raspberrypi4 as it is used as a platform to implement real-time image processing applications since their framework can leverage spatial and temporal parallelism. Integrate the Raspberry Pi board into the drone. The classified images are received in a telegram at the terminal. The accuracy of the model is 88%.

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Section: B Methodologymentioning

confidence: 99%

Onboard Processing of Drone Imagery for Military Vehicles Classification Using Enhanced YOLOv5

Raj,

Sahithi

et al. 2023

JAIT

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“…The spectral imaging is widely utilized in satellite imaging, where it is used to differentiate between different buildings, roads and vegetable farms and other geographical information. Further it is also used for to identify many objects like road or vehicles in territorial field [8]. The spectral imaging has huge applications in chemical field, medical field as well as quality inspection of agriculture on an early stage such as apples [3] also in citrus black spot detection [9].…”

Section: A Hyper Spectral Imagingmentioning

confidence: 99%

“…The non-destructive abilities of apples were measured using the intensity of penetration of light through apple skin in order to gather information about the apple tissues analyzing the quality assessment of apples [7]. Fungal disease like the citrus black spot was detected using correlation analysis and pattern recognition using NDVI band ratio method which detected the disease responsible for premature dropping of fruits from trees [8].…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Fruits using Hyper Spectral Images

Kumar¹,

Kapil²,

Gupta³

2019

IJTSRD

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One of the biggest problems in hyper spectral image analysis is the wavelength selection because of the immense amount of hypercube data. In this paper, we introduce an approach to find out the optimal wavelength selection in predicting the quality of the fruit. Hyper spectral imaging was built with spectral region of 400nm to 1000nm for fruit defect detection. For image acquisition, we used fluorescent light as the light source. Analysis was performed in visible region, which had spectral from 413nm to 642nm it was done because of the low reflectance spectrum found in fluorescent light sources. The captured image in this experiment demonstrates irregular illumination that means half of the fruit has brighter area. Analysis of the hyper spectral image was done in order to select diverse wavelengths that could possibly be used in multispectral imaging system. Selected wavelengths were used to create a separate image and each image went through thresholding. Experiment shows a multispectral imaging system which is able to detect defects in fruits by selecting most contributing wavelengths from the hyper spectral image. Algorithm presented in this paper could be improved with morphology operations so that we could get the actual size of the defect.

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“…Hyperspectral remote sensing images are rich in spatial, spectral and radiation information, including hundreds or even thousands of spectral bands, which can fully reflect the subtle features of the surface object spectrum and provide extremely rich information for the extraction of surface object information, which is beneficial to more detailed surface object classification [1][2][3][4]. In recent years, hyperspectral remote sensing images have attracted the attention of many scholars, and have been widely used in ecological monitoring [5,6], medical diagnosis [7,8], military reconnaissance [9] and other important fields. Due to the increase of spectral bands in hyperspectral remote sensing images, the problems of increased dimension and high data redundancy appear [10,11], resulting in the complexity of data processing [12].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

Ding

Shao

et al. 2021

Electronics

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Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order contractive auto-encoder with nuclear norm constraint (CAE-HNC) is proposed. By introducing Jacobian matrix in the CAE of the nuclear norm constraint, the nuclear norm has better sparsity than the Frobenius norm and can better describe the local low dimension of the data manifold. At the same time, a second-order penalty term is added, which is the Frobenius norm of the Hessian matrix expressed in the hidden layer of the input, encouraging a smoother low-dimensional manifold geometry of the data. The experiment of hyperspectral remote sensing image shows that CAE-HNC proposed in this paper is a compact and robust feature representation method, which provides effective help for the ground object classification and target recognition of hyperspectral remote sensing image.

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Identification of military vehicles in hyper spectral imagery through spatio-spectral filtering

Cited by 6 publications

References 12 publications

Onboard Processing of Drone Imagery for Military Vehicles Classification Using Enhanced YOLOv5

Onboard Processing of Drone Imagery for Military Vehicles Classification Using Enhanced YOLOv5

Anomaly Detection in Fruits using Hyper Spectral Images

Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

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