Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Duhayyim, Mesfer Al; Alsolai, Hadeel; Hassine, Siwar Ben Haj; Salama, Ahmed S.; Motwakel, Abdelwahed; Yaseen, Ishfaq; Zamani, Abu Sarwar

doi:10.32604/cmc.2023.033054

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Reliable data about developing crops with various climatic conditions and agricultural resources and with distinct time stamps are highly significant and beneficial for agricultural expansion [5]. Improved RS technologies comprising HSI can fill the gap with outcomes namely crop classification and offer an appropriate performance [6].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification

Alajmi,

Mengash,

Eltahir

et al. 2023

IEEE Access

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Hyperspectral imaging (HSI) plays a major role in agricultural remote sensing applications. Its data unit is the hyperspectral cube that contains spatial data in 2D but spectral band data of all the pixels in 3D. The classification accuracy of HSI was significantly enhanced by deploying either spatial or spectral features. HSIs are developed as a significant approach to achieve growth data monitoring and distinguish crop classes for precision agriculture, based on the reasonable spectral response to the crop features. The latest developments in deep learning (DL) and computer vision (CV) approaches permit the effectual detection and classification of distinct crop varieties on HSIs. At the same time, the hyperparameter tuning process plays a vital role in accomplishing effectual classification performance. The study introduces a dandelion optimizer with deep transfer learning-based crop type detection and classification (DODTL-CTDC) technique on HSI. The DODTL-CTDC technique makes use of the Xception model for the extraction of features from the HSI. In addition, the hyperparameter selection of the Xception model takes place using the DO algorithm. Moreover, the convolutional autoencoder (CAE) model is applied for the classification of crops into distinct classes. Furthermore, an arithmetic optimization algorithm (AOA) can be employed for optimal hyperparameter selection of the CAE model. The performance analysis of the DODTL-CTDC technique is assessed on the benchmark data set. The experimental outcomes demonstrate the betterment of the DODTL-CTDC method in the crop classification process.

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Section: Introductionmentioning

confidence: 99%

Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification

Alajmi,

Mengash,

Eltahir

et al. 2023

IEEE Access

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“…Jain P. et al, (2020) elaborate the broadly utilized for several facets of the DRM, extending from vulnerability to rapid damage valuations, for various areas ranging from coastal environments to complicated urban settings, and for disasters as different as cyclones or landslides. Duhayyim M.A. et al, (2023) developed the RS to provide information regarding the forthcoming disaster and event obstacles, along with an extraction of the flood region's features.…”

Section: Introductionmentioning

confidence: 99%

Integrated Disaster Risk Management for Flood Detection on Remote Sensing Images using Deep Learning techniques

2023

Global NEST Journal

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<p>Floods are one of the leading causes of damage, prompting mortality and substantial destruction to the structure and total economy of the affected nations. Remote sensing, satellite imagery, global positioning system, and geographic information system (GIS) are widely employed for flood identification to examine flood-related losses. Recently, accurate and automated flood detection models using remote sensing images have become effective for flood disaster management, risk manager, infrastructure planning, disaster rescue management, etc. Computer vision and deep learning (DL) models provide prompt and rapid flood detection in remote sensing images. In this aspect, this paper presents a multiverse optimization with a deep transfer learning-enabled flood detection (MVODTL-FD) technique for disaster risk management. In the proposed MVODTL-FD technique, remote sensing images are investigated for the effectual detection of floods. To accomplish this, the presented MVODTL-FD technique applies a guided normal filter (GNF) based image preprocessing approach to eliminate the noise. In addition, the proposed MVODTL-FD technique uses a deep convolutional neural network-based Squeeze Net model for feature extraction, and the hyperparameter process is performed using the MVO algorithm. At last, the flood detection process is performed using support vector machine (SVM) classification. For establishing the improved version of the MVODTL-FD method, a wide-ranging experimental analysis is performed. The MVODTL-FD model is rated higher in the comparative analysis than other DL models.</p>

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A U-Net Based Approach for High-Accuracy Land Use Land Cover Classification in Hyperspectral Remote Sensing

Khan,

Patil,

Vibhute

et al. 2024

Communications in Computer and Information Science

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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Cited by 7 publications

References 27 publications

Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification

Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification

Integrated Disaster Risk Management for Flood Detection on Remote Sensing Images using Deep Learning techniques

A U-Net Based Approach for High-Accuracy Land Use Land Cover Classification in Hyperspectral Remote Sensing

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