This study explores the use of an optimized 3D-2D convolutional neural network (CNN) model for efective mineral identification in the Djebel Meni region of Northwestern Algeria, utilizing hyperspectral imaging data from NASA's Hyperion EO-1 sensor. Given the challenges posed by remote, complex geological terrains, our approach integrates advanced deep-learning techniques with hyperspectral data to enhance mineral classification accuracy. Following atmospheric correction using the Quac module, spectral signatures of the target minerals-illite, kaolinite, and montmorillonite-from the United States Geological Survey (USGS) spectral library were employed as reference inputs. By leveraging this corrected hyperspectral data, the 3D-2D CNN model was trained to classify these clay minerals with high precision, achieving an overall accuracy of 94.26% and an average class-specific accuracy of 93.93%. These results highlight the model's robustness in diferentiating mineral compositions in geologically challenging contexts, even when limited ground truth data is available. This research underscores the potential of combining hyperspectral remote sensing with sophisticated CNN architectures to advance mineral identification and geospatial analysis, ofering valuable insights for mineralogical studies in similar remote regions.
Identifying and classifying minerals in remote and geologically complex terrains is a formidable
challenge, especially in hard-to-access regions. Traditional field-based mineral identification methods are
often limited due to high costs, time constraints, and logistical barriers, which restrict the collection
of comprehensive data across extensive areas [
NASA’s launch of the EO-1 Hyperion sensor in November 2000 marked a new era in spaceborne
hyperspectral mapping capabilities, transforming the field of remote mineral exploration. The Hyperion
sensor provides a spectral range of 0.4 to 2.5 across 242 spectral bands with a spectral resolution of
approximately 10 nm and a spatial resolution of 30 meters, making it highly suited for geological studies
[
With the rapid advancement of artificial intelligence, deep learning models have proven particularly
efective in analyzing the high-dimensional datasets generated by hyperspectral imaging [
In this study, we aim to develop and evaluate an optimized 3D-2D CNN model for the precise
classification of clay minerals—illite, kaolinite, and montmorillonite—using hyperspectral imaging
data from the Djebel Meni region in Algeria. The primary objective is to demonstrate that the hybrid
combination of 3D and 2D convolutional operations can efectively capture hyperspectral data’s spectral
and spatial features, thereby improving classification performance. To ensure the reliability of our
results, validated mineral classifications derived from the spectral information divergence (SID) method
and the USGS spectral library were used as ground truth [
The remainder of this paper is structured as follows: Section 2 presents the study area and materials, detailing the region and hyperspectral datasets used. Section 3 describes the methodology, focusing on the data preprocessing, SID validation, and CNN architecture. Section 4 discusses the results, including classification accuracy and comparative analysis, and section 5 concludes with insights into the implications of our findings and potential directions for future research.
The study area is located in Northwestern Algeria, between latitudes 36∘ 04′28.06′′ and 36∘ 03′45.11′′
and longitudes 0∘ 23′15.02′′ and 0∘ 31′08.59′′. It spans approximately 125 2, with an average
elevation between 100 and 200 meters. The climate is semi-arid, with annual rainfall averaging around
350 mm. Geologically, the region is largely composed of claystone formations, making it suitable for
mineralogical research, particularly for the study of minerals like illite, kaolinite, and montmorillonite
[
Djebel Meni, a hill in the Atlas Mountains, reaches an elevation of 313 meters (1, 027 feet) with a
prominence of 171 meters (561 feet). The site includes small open-pit mines and quarries for bentonite
extraction, emphasizing its economic and geological significance. The availability of hyperspectral
imagery from the Hyperion sensor also contributed to the selection of this area, facilitating remote
mineral identification and geological mapping [
Figure 1 provides a visual overview of the study area; the left panel shows the geological map of Hadjadj (ex. Bosquet) based on Jacob’s 1902 survey, illustrating various formations, including Helvetian clays and sandstone; the right panel displays the area on Google Earth, indicating key landmarks like Djebel Meni and surrounding features.
This study uses data from the Hyperion sensor aboard the EO-1 satellite, launched by NASA on November 21, 2000. The satellite orbits the Earth at an altitude of 705 km and passes over the same regions at the same local time, allowing for consistent comparisons.
The Hyperion sensor captures light reflected from the Earth’s surface using two spectrometers: • VNIR (Visible and Near-Infrared): Measures wavelengths from 0.355 to 1 across 70 spectral bands. • SWIR (Short-Wave Infrared): Measures wavelengths from 0.9 to 2.5 across 172 spectral bands.
Hyperion records 242 spectral bands with a 10 interval between them. The images have a spatial
resolution of 30 , enabling detailed soil and rock composition analysis in small areas [
The specific data used in this study has the following characteristics: • Acquisition date: December 17, 2010 • Spatial resolution: 30 • Spectral resolution: 10 • Number of bands: 242
Thanks to its high spectral resolution, Hyperion data allows for the identification of specific mineral
signatures, such as illite, kaolinite, and montmorillonite, even in geologically complex areas like Djebel
Meni [
The proposed methodology aims to eficiently process hyperspectral data and extract valuable geological insights through a series of well-defined steps. As illustrated in figure 2, the process begins with data preprocessing, which includes essential corrections such as bad bands removal to eliminate noisy and irrelevant spectral information, radiometric calibration to address sensor-induced distortions, and atmospheric correction to reduce the impact of atmospheric interference. Following preprocessing, dimensionality reduction is performed using principal component analysis (PCA) to simplify the dataset while preserving key spectral features. The next step involves image segmentation through the SID method, which isolates distinct mineralogical zones within the hyperspectral image. Subsequently, data modeling is carried out using a combination of 3D and 2D CNNs to analyze and classify the data efectively. Finally, the methodology concludes with evaluation and comparisons, where the results are assessed, and the proposed approach is benchmarked against other techniques to validate its performance and reliability. The workflow is designed to ensure an accurate and comprehensive analysis of hyperspectral data.
Bad bands removal Atmospheric
correction Data preprocessing
Radiometric calibration Dimensionality reduction (PCA)
Segmented image (SID) Data modeling (3D-2D CNN) Data evaluation and comparisons
Data preprocessing is crucial in hyperspectral image analysis, ensuring the data is clean and reliable
for further processing. One essential task in this phase is bad band removal (BBR), which focuses on
identifying and excluding spectral bands afected by noise, sensor artefacts, or atmospheric interference
[
• Ozone (2): Impacts the VNIR region below 0.4 , where the signal is weak and noisy.
This study identified bad bands through spectral analysis, removed them from the dataset, and verified them to ensure that only relevant spectral information was retained. Table 1 summarizes the bands removed, their corresponding spectrometer, wavelength ranges, and reasons for exclusion.
Hyperspectral images acquired by the Hyperion EO-1 sensor contain closely spaced spectral bands,
which can introduce radiometric errors. Radiometric, geometric, and atmospheric correction is required
to ensure accurate use of the data [
Radiometric calibration and atmospheric correction are essential steps in processing hyperspectral
data, as atmospheric conditions significantly influence remote sensing measurements. Scattering and
absorption by atmospheric gases and particulates alter the light reaching the sensor, with water vapour
being the primary contributor, followed by gases such as carbon dioxide and ozone [
QUAC determines atmospheric parameters directly from the observed pixel spectra in the image
without requiring external information. While it is less precise than physics-based methods like
FLAASH, QUAC typically produces ’reflectance spectra, which measure the proportion of incident light
reflected by a surface, with an accuracy of about 10% relative to ground truth [
For the classification of hyperspectral images in the Djebel Meni region (Northwestern Algeria) using Hyperion EO-1 data, dimensionality reduction is a crucial preprocessing step. Instead of relying on methods like PCA, we reduced the number of spectral bands to 30 by selecting those containing the most valuable information for classifying the three minerals of interest: Illite, Kaolinite, and Montmorillonite. This targeted band selection preserves critical features for classification while discarding redundant or less informative bands, optimizing the dataset for subsequent analysis.
By integrating this reduced dataset into the 3D-2D CNN architecture, we ensured eficient
computation, faster model convergence, and improved classification accuracy. This approach enhances the
network’s ability to extract and analyze meaningful spectral-spatial features relevant to the identification
of the selected minerals [
The segmentation process is a critical step in hyperspectral image analysis. It aims to identify and isolate specific mineral signatures within the hyperspectral data by dividing the image into distinct regions corresponding to diferent mineral classes. This step is essential for highly precisely classifying and mapping minerals in complex geological environments.
This study employs the spectral information divergence (SID) algorithm as the primary segmentation
tool. SID is widely recognized for its robustness in detecting spectral variations by measuring the
divergence between the spectral signature of each pixel and reference spectra. Unlike simpler methods
such as spectral angle mapper (SAM), SID accounts for subtle spectral diferences, making it particularly
efective for identifying minerals with overlapping spectral features [
The SID algorithm operates as follows: • Input spectral data: Each pixel in the hyperspectral image contains a spectrum representing the reflectance values at multiple wavelengths. • Reference spectra: Spectral signatures from the USGS library are reference inputs for illite, kaolinite, and montmorillonite. • Comparison: SID calculates the divergence between each pixel’s spectrum and the reference spectra. Lower divergence values indicate a higher likelihood that the pixel belongs to a specific mineral class.
As illustrated in digure 3, the segmentation process can be divided into three main components: • Spectral signatures (figure 3.a): The unique reflectance patterns of illite, kaolinite, and montmorillonite are extracted from the USGS dataset. These patterns serve as the basis for segmentation, as each mineral exhibits distinct spectral characteristics in specific wavelength ranges. • Input hyperspectral image (figure 3.b): The preprocessed hyperspectral image serves as the input to the SID algorithm. It contains all spectral bands retained after preprocessing, such as bad band removal and atmospheric correction. • Segmented map (figure 3.c): The output of the SID algorithm is a segmented map where a distinct colour represents each mineral class: – Red: Illite – Green: Kaolinite – Blue: Montmorillonite • Illite: 4,775 pixels • Kaolinite: 12,042 pixels • Montmorillonite: 24,410 pixels
The segmented map includes pixel samples for each mineral class, which serve as ground truth for model evaluation. The number of pixels assigned to each class is as follows:
This segmentation process enables precise mineral identification by leveraging the high spectral resolution of Hyperion data and the advanced capabilities of the SID algorithm.
Our study introduces a hybrid convolutional neural network (CNN) architecture aimed at classifying minerals from the Djebel Meni region. This approach leverages a 3D-2D CNN framework designed to efectively capture both spatial and spectral features inherent in hyperspectral imagery. The architecture incorporates four 3D convolutional layers tailored to extract spatial and spectral details. Specifically, the kernel sizes for these layers are defined as follows: • First layer: 8 × 3 × 3 × 3 × 1 (11 = 3, 21 = 3, 31 = 3). • Second layer: 16 × 3 × 3 × 3 × 8 (12 = 3, 22 = 3, 32 = 3). • Third layer: 16 × 3 × 3 × 3 × 16 (13 = 3, 23 = 3, 33 = 3).
• Fourth layer: 32 × 3 × 3 × 3 × 16 (14 = 3, 24 = 3, 34 = 3).
The 2D segment of the model consists of three 2D convolutional layers with the following kernel configurations: • First layer: 32 × 3 × 3 × 32 (11 = 3, 21 = 3). • Second layer: 16 × 3 × 3 × 32 (12 = 3, 22 = 3).
• Third layer: 8 × 3 × 3 × 16 (13 = 3, 23 = 3).
The output from the seventh layer is flattened, and all neurons are fully connected to the next layer with 64 neurons, culminating in a classification layer that outputs predictions for 3 mineral classes. A detailed summary of the model, including layer types, output dimensions, and parameter counts, is provided in table 2. Notably, the initial dense layer contains the highest number of parameters, and the total parameter count for the model depends on the number of classes in the dataset. For the hyperspectral dataset, the proposed model includes 301, 947 trainable parameters.
Extensive experimental evaluations to optimize the classification of minerals in hyperspectral images drove the choice of the hybrid 3D-2D CNN architecture. This architecture demonstrated superior performance, particularly in mineral-rich regions such as Cuprite, due to its ability to eficiently extract spatial and spectral features. Specifically, the 3D convolutional layers efectively capture spectral dependencies, while the 2D layers enhance spatial feature representation, improving classification accuracy.
The number of layers and filter sizes was carefully determined based on iterative testing to balance
computational complexity and classification performance. For instance, smaller kernel sizes in the 3D
layers ensured precise spectral feature extraction, while larger kernel sizes in the 2D layers improved the
spatial generalization of mineral patterns. The proposed design was validated on diverse hyperspectral
datasets, confirming its robustness and applicability for mineral identification tasks [
The model’s weights are initialized randomly and optimized using the backpropagation algorithm with the Adam optimizer. Softmax activation is employed for classification purposes. The network is trained over 100 epochs with a batch size of 256 samples and a learning rate of 0.001 without employing data augmentation techniques. This hybrid CNN efectively captures the spatial and spectral richness of hyperspectral images, utilizing 3D convolutions to harness spectral depth and 2D convolutions to refine spatial features.
Our experimental work was conducted on a robust computing setup featuring an Intel Core i7-12700F processor paired with 64 GB of RAM. To further enhance computational performance and ensure reproducibility, we utilized a GeForce RTX 3070 Ti GPU. For all prior methods, the spatial dimensions were standardized by extracting 3D patches of size 25×25×30. Each patch was processed independently as an image, with the central pixel representing the target mineral class. This approach facilitated the efective learning and extraction of spatial and spectral features.
The dataset was randomly split into three subsets: 70% for training, 10% for validation, and 20% for testing. To ensure a balanced representation of all mineral classes, we selected a total of 4000 samples per class, distributed as follows: 2800 samples for training (70%), 400 samples for validation (10%), and 800 samples for testing (20%). Special care was taken to prevent overlap between image plots across these subsets, ensuring no information leakage between the training and test sets. This precaution was critical to preserving the independence of the test data and preventing any bias in the evaluation process.
Overfitting poses a significant challenge in hyperspectral mineral classification, as it undermines the ability of CNN models to classify unseen data accurately. To mitigate this issue, we implemented a series of optimization techniques, including batch normalization, L2 regularization, learning rate scheduling, dropout, and K-fold cross-validation. These strategies work synergistically to enhance learning stability and ensure robust generalization, enabling the model to achieve reliable classification performance. The hyperparameters used in this study, detailed in table 3, were fine-tuned based on preliminary experiments to strike a balance between optimal performance and consistent evaluation across methods.
In this study, we employed various metrics to evaluate the performance of our classification approach.
In addition to overall accuracy (OA), we used the Kappa coeficient to assess the agreement between
predicted and actual classifications while accounting for chance agreement. We also calculated the
average accuracy (AA) to provide class-specific evaluations, highlighting potential performance disparities
across diferent classes [
+ + + +
1 = ×
∑︁
=0 + = 0 −
1 −
Additionally, the evaluation incorporates F1-score, precision, and recall metrics to provide a more comprehensive understanding of model performance across diferent dimensions. These metrics are defined as follows: 1− = = =
+
+ 2 × × + (5) (6) (7)
The classification results obtained for the Djebel Meni region demonstrate the robustness and efectiveness of our approach based on a 3D-2D CNN network. Figure 4 shows the model’s training curves, illustrating the evolution of accuracy and loss over 100 training epochs. A maximum accuracy of 99.92% and a minimum loss of 0.0415 was achieved, highlighting the model’s exceptional learning ability and instilling confidence in its performance. This performance reflects the model’s ability to capture the complex spectral characteristics of minerals while minimizing errors. The stability of the curves, with no signs of divergence, testifies to optimized training supported by techniques such as regularization, dropout, and batch normalization. However, further analysis of test data is crucial to assess the generalizability and robustness of the model in the face of novel data, highlighting the ongoing importance of research in this field.
The results in table 4 highlight the performance metrics of the proposed 3D-2D CNN model for mineral classification in the Djebel Meni region. The average precision 0.94, recall 0.93, and F1-score 0.94 emphasize the model’s robustness. Among the classes, Illite achieved the highest scores, with a precision of 0.97, recall of 0.95, and F1-score of 0.96, demonstrating exceptional accuracy in identifying this mineral. Kaolinite and Montmorillonite also showed strong performance, with F1-scores of 0.90 and 0.95, respectively. The overall accuracy of 94.26% and Kappa coeficient 0.9401 indicate high consistency and agreement between predictions and ground truth labels.
The confusion matrix provides further insights into the classification performance. Illite had 758 correctly classified samples, with only 41 misclassifications as Montmorillonite and 1 misclassification as Kaolinite. Kaolinite achieved 729 correct predictions but experienced 71 errors, mostly misclassified Classes Ilite Kaolinite Montmorillonite Average Overall accuracy Average accuracy Kappa
Precision 0.97 0.89 0.95 0.94 as Montmorillonite. Montmorillonite had 750 correctly predicted samples, with 5 misclassified as Illite and 45 as Kaolinite.
The relatively low number of misclassified samples across all classes demonstrates the efectiveness of the proposed model in minimizing errors. However, the higher confusion between Kaolinite and Montmorillonite (71 and 45 errors) suggests overlapping spectral characteristics that require additional features or improved preprocessing to distinguish.
The applied optimization strategies, including dropout, L2 regularization, and batch normalization, efectively reduced overfitting, contributing to the model’s strong generalization capabilities. These results confirm the suitability of the 3D-2D CNN model for robust hyperspectral mineral classification, even in complex geological contexts such as the Djebel Meni region.
Table 5 provides a comprehensive comparison of our proposed 3D-2D CNN model with state-of-theart techniques, including SAM, 2D-CNN, and 3D-CNN, for mineral classification in the Djebel Meni region. The results clearly demonstrate the advantages of our approach, which not only surpasses traditional methods but also addresses key challenges in hyperspectral mineral classification. For the Illite class, our model achieved the highest accuracy (0.9607), significantly outperforming SAM ( 0.9036), 2D-CNN (0.9345), and 3D-CNN (0.9483). This highlights the model’s ability to capture subtle spectral and spatial variations. The Kaolinite class, known for its spectral similarities with other minerals, exhibited a substantial improvement in classification accuracy with our method ( 0.9054), compared to SAM (0.8543) and even 3D-CNN (0.8963). This improvement underlines the efectiveness of the hybrid 3D-2D CNN architecture in distinguishing closely related mineral classes. For Montmorillonite, our approach achieved an accuracy of 0.9517, outperforming SAM (0.9087) and 2D-CNN (0.9237).Moreover, the overall accuracy (OA) and average accuracy (AA) metrics further validate the superiority of our method. With an OA of 0.9393 and an AA of 0.9426, our model consistently outperformed SAM (0.8889, 0.8957), 2D-CNN (0.9120, 0.9196), and 3D-CNN (0.9283, 0.9312). These results confirm that the integration of 3D and 2D convolutional operations provides a balanced trade-of between spectral and spatial feature extraction, enabling enhanced classification performance.
In contrast to previous methods that rely on traditional machine learning models or standalone 2D/3D CNN architectures, our approach introduces a novel hybrid architecture specifically optimized for hyperspectral data. This innovation allows our model to overcome the limitations of earlier techniques, such as their inability to fully exploit the multidimensional nature of hyperspectral data. Additionally, by extending the application of 3D-2D CNNs to mineral classification—previously applied predominantly in vegetation studies—our work broadens the scope of hyperspectral imaging research and demonstrates the versatility of this architecture. In conclusion, the proposed method consistently delivers superior performance across all classes and evaluation metrics. These results confirm the robustness and reliability of our approach, ofering a powerful and reproducible framework for mineral classification in hyperspectral imaging. By addressing both spectral and spatial complexities, our work sets a new benchmark for hyperspectral mineral mapping and opens new avenues for exploration in geologically complex terrains.
This study presented a 3D-2D CNN model for mineral classification using hyperspectral imaging data from NASA’s Hyperion EO-1 sensor in the Djebel Meni region. The proposed approach achieved superior performance compared to state-of-the-art methods, with an overall accuracy of 94.26% and robust classification of illite, kaolinite, and montmorillonite. These results underscore the efectiveness of combining spatial and spectral feature extraction for reliable mineral identification in geologically complex terrains. Looking ahead, we are excited about the potential for future exploration of improved CNN architectures to enhance classification accuracy further and address more complex terrains with a greater number of mineral classes.
Declaration on Generative AI: During the preparation of this work, the authors used the following tools: • DeepL for intelligent translation and grammar correction. • Grammarly for grammar checks, spelling correction, and plagiarism detection.
• ChatGPT-4 for improving writing style, verifying grammar, and ensuring the logical flow of ideas. No images were generated using AI tools.
After using these tools/services, the authors reviewed and edited the content as needed and takes full responsibility for the publication’s content.