Reduced 3-D Deep Learning Framework for Hyperspectral Image Classification

Laban, Noureldin; Abdellatif, Bassam; Ebeid, Hala M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.1007/978-3-030-14118-9_2

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…In our work, we have used four HSI datasets which are used for analyzing our proposed CSDCNN-AO technique. Here, we use Houston U (HU) dataset [ 33 ], Indiana Pines (IP) [ 34 ], Kennedy Space Center (KSC) [ 35 ], and Salinas Scene (SS) dataset [ 17 ]. In the case of the IP dataset, the size of the dataset is 145 × 145.…”

Section: Resultsmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

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The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy.

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Section: Resultsmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

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“…The input HSI cube having dimension MxNxR is first sent into a layer of Factor Analysis (FA) to reduce the dimension into MxNxB. Reducing the dimension reduces training time by 60% [17]. The output vector Y having a dimension 1xMN take up a class from the available land cover categories denoted by C. The spectral dimensions are preserved in FA, i.e.…”

Section: B Spectralnet Model Descriptionmentioning

confidence: 99%

“…In the following works [16], residual connections were added to a 3D CNN in order to assimilate both high and low level features present in a HSI and improve classification results. The work of [17], studied the effect of dimensionality reduction of HSI on 3D CNNs.…”

Section: Introductionmentioning

confidence: 99%

SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification

Chakraborty,

Trehan

2021

Preprint

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Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multiresolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatialspectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods. The code is publicly available in https://github.com/tanmay-ty/SpectralNET.

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“…Unlike the common 3-D-CNN, the proposed pseudo-3-D modules can take both spatial and spectral features at the same time. In [31], 3-D-CNN method has been used and the effects of reduction of dimension has been investigated. It shows that the training time reduced by about 60%.…”

Section: Introductionmentioning

confidence: 99%

Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms

Farmonov

Amankulova

Szatmári

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Developments in space-based hyperspectral sensors, advanced remote sensing, and machine learning can help crop yield measurement, modelling, prediction, and crop monitoring for loss prevention and global food security. However, precise and continuous spectral signatures, important for large-area crop growth monitoring and early prediction of yield production with cutting-edge algorithms, can be only provided via hyperspectral imaging. Therefore, this article used new-generation Deutsches Zentrum für Luft-und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) images to classify the main crop types (hybrid corn, soybean, sunflower, and winter wheat) in Mezőhegyes (southeastern Hungary). A Wavelet-attention convolutional neural network (WA-CNN), random forest and support vector machine (SVM) algorithms were utilized to automatically map the crops over the agricultural lands. The best accuracy was achieved with the WA-CNN, a feature-based deep learning algorithm and a combination of two images with overall accuracy (OA) value of 97.89%and the user's accuracy producer's accuracy was from 97% to 99%. To obtain this, first, factor analysis was introduced to decrease the size of the hyperspectral image data cube. A wavelet transform was applied to extract important features and combined with the spectral attention mechanism CNN to gain higher accuracy in mapping crop types. Followed by SVM algorithm reported OA of 87.79%, with the producer's and user's accuracies of its classes ranging from 79.62% to 96.48% and from 79.63% to 95.73%, respectively. These results demonstrate the potentiality of DESIS data to observe the growth of different crop types and predict the harvest volume, which is crucial for farmers, smallholders, and decision-makers.

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Reduced 3-D Deep Learning Framework for Hyperspectral Image Classification

Cited by 10 publications

References 20 publications

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification

Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms

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