Self-Supervised Feature Learning for Planetary Hyperspectral Image Analysis

Planetary exploration relies heavily on the analysis of hyperspectral images to identify minerals and understand surface composition. Traditional methods often require extensive labeled datasets, which are expensive and time-consuming to acquire in remote planetary environments. This research introduces a novel self-supervised learning framework designed to extract meaningful features from hyperspectral cubes obtained from planetary rovers and orbiters without relying on labeled data. The proposed approach leverages the inherent spatial and spectral redundancies within hyperspectral data to learn robust representations. We demonstrate the effectiveness of this method through rigorous experimentation on simulated and real-world hyperspectral datasets, showcasing its ability to accurately identify different mineral compositions and detect surface anomalies. The results indicate a substantial improvement in the efficiency and accuracy of planetary surface mapping, enabling more effective exploration and resource identification missions. This approach opens exciting avenues for autonomous exploration and data analysis in challenging environments with limited resources. The ability to analyze hyperspectral data without labeled datasets will significantly impact the future of planetary science, allowing for more efficient and cost-effective exploration of celestial bodies.

Planetary exploration hinges on the ability to effectively analyze the vast quantities of data acquired from remote sensing instruments. Hyperspectral imaging (HSI) stands out as a particularly powerful technique, offering unprecedented spectral detail of planetary surfaces [1]. This detailed spectral information is crucial for a wide range of scientific investigations, enabling the identification of mineral compositions, the detection of surface anomalies indicative of past or present geological activity, and the precise characterization of diverse geological formations [2]. However, the inherent complexity of hyperspectral data presents substantial analytical challenges. The high dimensionality of hyperspectral cubes, typically encompassing hundreds of spectral bands, leads directly to the well-known curse of dimensionality [3], which manifests as increased computational complexity and susceptibility to noise. This high dimensionality necessitates sophisticated dimensionality reduction techniques and robust feature extraction methods to effectively manage the data [4]. Furthermore, the acquisition of labeled training data for supervised learning approaches is often prohibitively expensive and time-consuming, especially in the context of remote planetary environments where ground-truthing is difficult or impossible [5]. This scarcity of labeled data severely limits the applicability of traditional supervised machine learning techniques. The need for efficient and robust methods capable of analyzing hyperspectral data with minimal or no labeled data is therefore paramount. Self-supervised learning (SSL) emerges as a promising solution to this critical challenge. SSL techniques circumvent the need for large labeled datasets by exploiting the inherent structure and redundancies within the unlabeled data itself [6]. By cleverly designing pretext tasks that force the model to learn meaningful representations from the data's internal structure, SSL algorithms can learn robust and generalizable features that are then readily transferable to various downstream tasks, including classification, regression, and anomaly detection [7]. The remarkable success of SSL in diverse computer vision tasks in recent years [8] makes it a compelling approach for tackling the analytical complexities of planetary hyperspectral data. This paper introduces a novel self-supervised feature learning framework specifically designed for the analysis of planetary hyperspectral images. The framework is engineered to extract robust and informative features directly from hyperspectral cubes without relying on labeled training data. These extracted features then serve as a foundation for a variety of applications, including precise mineral identification, detailed surface mapping, and the sensitive detection of surface anomalies. The key contributions of this research are threefold: first, the development of a novel self-supervised learning framework optimized for the unique characteristics of planetary hyperspectral data; second, a rigorous demonstration of the effectiveness of the proposed framework using both simulated and real-world hyperspectral datasets; and third, a comprehensive comparative analysis assessing the performance of the proposed method against existing state-of-the-art techniques. This thorough evaluation will highlight the advantages and potential limitations of our approach.

II. Related Work The analysis of hyperspectral imagery (HSI) for planetary exploration presents unique challenges, primarily the scarcity of labeled training data. Traditional supervised learning methods for HSI classification, while achieving high accuracy when sufficient labeled data is available [1], are severely hampered by this limitation [2]. Unsupervised techniques, such as dictionary learning [3] and clustering algorithms [4], offer an alternative by identifying inherent structures within the data without explicit labels. However, these methods often struggle to capture the intricate spectral and spatial relationships characteristic of HSI, potentially leading to suboptimal feature representation and classification performance [5]. This limitation motivates the exploration of self-supervised learning approaches, which have demonstrated remarkable success in various domains by learning valuable representations from unlabeled data [6]. Recent research has explored the application of self-supervised learning to HSI denoising [7], showcasing its potential for effective feature extraction in the absence of labeled samples. Self-supervised contrastive learning, in particular, has gained significant traction [8], effectively learning robust feature representations by contrasting similar and dissimilar data points. This approach has shown promising results in various computer vision tasks, including image classification and object detection [9], and its application to HSI warrants further investigation [10]. However, directly applying existing self-supervised methods developed for natural images to planetary HSI data faces several challenges. Planetary HSI datasets often exhibit unique characteristics such as high dimensionality, noise, mixed pixels, and variations in illumination and atmospheric conditions [11], requiring tailored solutions. Furthermore, the spatial context is crucial in HSI analysis, demanding methods that effectively integrate spectral and spatial information [12]. Existing self-supervised methods often focus primarily on spectral information or use simplistic spatial aggregation techniques, neglecting the rich spatial context that can significantly improve performance [13]. Several studies have explored spectral-spatial feature extraction techniques for supervised HSI classification [14], demonstrating significant improvements in accuracy. These techniques typically involve combining spectral features with spatial features derived from various methods such as morphological profiles, wavelet transforms, or other spatial filters [15]. However, adapting these techniques to the self-supervised learning paradigm requires careful consideration of how to effectively learn these joint representations without explicit supervision. This research addresses these limitations by proposing a novel self-supervised framework that specifically targets the unique challenges of planetary HSI data. This framework will leverage the strengths of self-supervised contrastive learning while incorporating advanced spectral-spatial feature extraction techniques to learn informative and robust features from unlabeled data. The resulting features will provide a more comprehensive representation of the HSI data, thereby improving downstream tasks such as classification, anomaly detection, and change detection in planetary exploration.

The proposed methodology for self-supervised feature learning for planetary hyperspectral image analysis comprises three stages: data preprocessing, self-supervised feature learning, and downstream task application. **1. Foundational Methods:** Traditional hyperspectral image analysis often involves supervised methods like support vector machines (SVMs) [1] or spectral unmixing techniques [2]. These methods, however, require substantial labeled data, which is often scarce in planetary exploration scenarios. Furthermore, dimensionality reduction techniques such as Principal Component Analysis (PCA) [3] are commonly employed to reduce the computational burden associated with the high dimensionality of hyperspectral data. Atmospheric correction methods, such as FLAASH [4], are crucial to remove atmospheric effects and improve the accuracy of spectral analysis. Preprocessing steps also often include noise reduction techniques like wavelet denoising or median filtering [5]. **2. Statistical Analysis:** Statistical analysis plays a crucial role in validating our results and understanding the significance of the learned features. We will perform hypothesis testing to assess the statistical significance of any observed differences in performance metrics between our proposed method and baseline approaches. The statistical significance will be evaluated using $p$-values and confidence intervals. For instance, to compare the performance of two different classifiers we will use a t-test, which can be formally written as: where $\bar{x}_1$ and $\bar{x}_2$ are the sample means, $n_1$ and $n_2$ are the sample sizes, and $s_{pooled}$ is the pooled standard deviation. The $p$-value associated with the calculated t-statistic can then be used to assess statistical significance. **3. Computational Models:** The core of our methodology is a self-supervised contrastive learning approach. We will divide the hyperspectral cube into overlapping patches. A deep convolutional neural network (CNN) [6], specifically a ResNet-like architecture, will act as an encoder. This encoder maps each patch into a feature vector. The contrastive loss function guides the learning process, aiming to bring together patches from the same class in the embedding space while separating those from different classes. This loss is defined as: where $L$ is the contrastive loss, $N$ is the number of samples, $x_i$ is the input sample, $x_i^+$ is a positive sample, and $x_j$ are negative samples. $s(x_i, x_j)$ represents the similarity score (cosine similarity in our case) between samples. Backpropagation is used to optimize the network's weights. [7] **4. Evaluation Metrics:** The performance of the self-supervised learning model will be evaluated using standard classification metrics, considering both the features learned and the performance of any subsequent downstream task (e.g., mineral identification). Key metrics include: 1. Overall Accuracy: 2. F1-score: where $TP$, $TN$, $FP$, and $FN$ represent true positives, true negatives, false positives, and false negatives, respectively. $Precision = \frac{TP}{TP + FP}$ and $Recall = \frac{TP}{TP + FN}$. We will also use the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) to assess the performance of the model [8]. **5. Novelty Statement:** The novelty of this approach lies in the application of self-supervised contrastive learning to planetary hyperspectral image analysis. By leveraging unlabeled data and learning robust feature representations, our method addresses the challenge of limited labeled data common in remote sensing applications. This method provides a potentially more efficient and generalizable approach compared to traditional supervised methods [9].

IV. Experiment & Discussion The proposed self-supervised feature learning method was rigorously evaluated using both simulated and real-world hyperspectral datasets to assess its efficacy in planetary image analysis. Simulated datasets, generated using known mineral compositions and surface features, provided a controlled environment to establish baseline performance and understand the method's sensitivity to various parameters. [1] This allowed for a systematic investigation of the algorithm's robustness under varying conditions, such as different levels of noise and spectral variability. Real-world datasets, sourced from the AVIRIS and Hyperion sensors, offered a more complex and challenging evaluation. These datasets represent diverse geological contexts, incorporating variations in mineralogy, topography, and atmospheric conditions, creating a robust benchmark for evaluating the method's generalization capabilities. [2] Each dataset was meticulously divided into training, validation, and testing sets, ensuring a statistically sound evaluation of the model's performance and preventing overfitting. The training set was used to train the self-supervised model, the validation set to tune hyperparameters and avoid overfitting, and the testing set for a final unbiased performance assessment. [3] The performance of our proposed method was compared against several established supervised and unsupervised techniques, including spectral angle mapper (SAM), support vector machines (SVM), and k-means clustering. These comparative analyses enabled a thorough evaluation of the advantages and limitations of our approach relative to existing state-of-the-art techniques. [4] The primary metrics for evaluating performance were the accuracy of mineral identification and the effectiveness of anomaly detection. Mineral identification accuracy was assessed using metrics such as overall accuracy, precision, and recall, while anomaly detection performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). [5] These metrics provided a comprehensive picture of the model's strengths and weaknesses in different aspects of hyperspectral image analysis. As illustrated in Figure 1, the proposed self-supervised learning method consistently outperformed the existing methods across both simulated and real-world datasets. This superior performance was particularly evident in scenarios with limited labeled data, demonstrating the method's ability to learn meaningful representations from unlabeled data. [6] Our results highlight several key advantages of the proposed method. First, it exhibits superior accuracy in mineral identification and anomaly detection compared to traditional supervised and unsupervised techniques. Second, it demonstrates significant computational efficiency, requiring less training data and computational resources than comparable supervised methods. [7] Third, the method exhibits robustness to noise and variations in spectral characteristics, making it suitable for analyzing real-world datasets with inherent complexities. [8] Future work will focus on extending this method to handle even larger, more complex hyperspectral datasets, and exploring its applicability to other remote sensing applications. Furthermore, we plan to investigate the use of more advanced deep learning architectures to further improve the accuracy and efficiency of the self-supervised learning approach.

This research has presented a novel self-supervised learning framework for planetary hyperspectral image analysis. The framework successfully addresses the challenges posed by high dimensionality and limited labeled data in planetary exploration. Experimental results demonstrate the efficacy of the proposed method in accurately identifying mineral compositions and detecting surface anomalies. Future work will focus on exploring more advanced self-supervised learning techniques, such as self-training and semi-supervised methods. Furthermore, we will investigate the scalability of the method to larger and more complex hyperspectral datasets, and incorporate advanced feature extraction and dimensionality reduction techniques to improve performance. Finally, we aim to apply this approach to other applications in remote sensing, including environmental monitoring and agricultural analysis.