Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification

Dutta, Ashit Kumar; Alsanea, Majed; Qureshi, Basit; Alghayadh, Faisal Yousef; Sait, Abdul Rahaman Wahab

doi:10.1080/07038992.2022.2089102

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…To demonstrate the superior performance of the HSOD-JSODL technique over other approaches, a detailed comparison study is given in Table 5 and Fig. 11 [23,24]. The experimental values ensured that the HSOD-JSODL technique reaches better performance over other models on both datasets.…”

Section: Performance Validationmentioning

confidence: 97%

Hyperspectral Object Detection Using Bioinspired Jellyfish Search Optimizer With Deep Learning

Mahgoub,

Albraikan,

Othman

et al. 2023

IEEE Access

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Hyperspectral images (HSI) provide a rich source of data for remote sensing applications, offering extensive spectral data about the Earth's surface. Object detection in HSI remains a challenging process with various application areas in environmental monitoring, agriculture, and geospatial analysis. The development of deep learning (DL) models for HSI object detection paves the way for new opportunities in advanced remote sensing analysis. DL models enable the automated and reliable detection of target objects. Particularly, convolutional neural networks (CNNs) can handle the high-dimensional nature of hyperspectral data and efficiently learn complex relationships among spectral patterns and object classes. This results in improved detection performance and reduces the need for manual feature engineering. Therefore, this study presents a Hyperspectral Object Detection using Bioinspired Jellyfish Search Optimizer with Deep Learning (HSOD-JSODL) technique for Enhanced Remote Sensing Analysis. The aim of the HSOD-JSODL method lies in the effectual recognition of interested objects in the HSI using the DL model. To achieve this, the HSOD-JSODL technique employs EfficientDet object detector to recognize various kinds of objects in the HSI. EfficientDet is a recently developed object detector which integrates efficiency via a compound scaling approach and efficient network design. For the classification of detected objects, the HSOD-JSODL technique uses a deep belief network (DBN) classifier model. To improve the object classification results of the DBN model, the JSO algorithm is applied as a hyperparameter optimizer. The simulation analysis of the HSOD-JSODL technique is examined on the HSI dataset, and the outcomes are examined under various measures. The simulation values portrayed the betterment of the HSOD-JSODL technique over compared methods. INDEX TERMSRemote sensing; Hyperspectral imaging; Deep learning; Object detector; Jellyfish search optimizer I. INTRODUCTION Object detection (OD) is a process of locating objects at distinct scales in imaged scenes and recognizing their category data. It is one of the essential yet difficult processes in modern computer vision (CV) study, with applications, namely medical diagnosis, autonomous driving, industrial machine vision, and satellite remote sensing [1]. Currently, the rapid growth of deep neural network (DNN) methods and largescale, well-annotated data have resulted in breakthroughs in image target recognition. Presently, many works on OD are dependent on imaging with visible light utilizing RGB images) [2]. However, in numerous scenarios, objects cannot This article has been accepted for publication in IEEE Access.

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Section: Performance Validationmentioning

confidence: 97%

Hyperspectral Object Detection Using Bioinspired Jellyfish Search Optimizer With Deep Learning

Mahgoub,

Albraikan,

Othman

et al. 2023

IEEE Access

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show abstract

“… CNNs in Remote Sensing : Traditional CNNs, while effective for many vision tasks, leverage pooling layers to gather context information, inevitably reducing the spatial resolution. This trade-off between spatial resolution and receptive field size is detrimental for RS, where the segmentation of smaller objects with high precision is of utmost importance [40] , [41] . In the subsequent sections, we delve into a novel approach that seeks to amalgamate the strengths of transformers and CNNs, aiming to address the unique challenges posed by RS images and enhance the granularity and accuracy of semantic segmentation [42] .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

Boulila,

Ghandorh,

Masood

et al. 2024

Heliyon

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“…Indices provide rapid solution to detect and monitor the spread of oil spill incidents in the vast expanses of oceans [ 17 , 18 ]. These mathematical combinations of different spectral bands simplify the complexity of raw satellite data, making it more accessible and understandable [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Novel oil spill indices for sentinel-2 imagery: A case study of natural seepage in Qaruh Island, Kuwait

Zakzouk,

El-Magd,

Ali

et al. 2024

MethodsX

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Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification

Cited by 4 publications

References 22 publications

Hyperspectral Object Detection Using Bioinspired Jellyfish Search Optimizer With Deep Learning

Hyperspectral Object Detection Using Bioinspired Jellyfish Search Optimizer With Deep Learning

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

Novel oil spill indices for sentinel-2 imagery: A case study of natural seepage in Qaruh Island, Kuwait

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