Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks

Abstract

Hyperspectral imaging has become a mature technology which brings exciting possibilities in various domains, including satellite image analysis. However, the high dimensionality and volume of such imagery is a serious problem which needs to be faced in Earth Observation applications, where efficient acquisition, transfer and storage of hyperspectral images are key factors. To reduce the time (and ultimately cost) of transferring hyperspectral data from a satellite back to Earth, various band selection algorithms have been proposed. They are built upon the observation that for a vast number of applications only a subset of all bands convey the important information about the underlying material, hence we can safely decrease the data dimensionality without deteriorating the performance of hyperspectral classification and segmentation techniques. In this paper, we introduce a novel algorithm for hyperspectral band selection that couples new attention-based convolutional neural networks used to weight the bands according to their importance with an anomaly detection technique which is exploited for selecting the most important bands. The proposed attention-based approach is data-driven, re-uses convolutional activations at different depths of a deep architecture, identifying the most informative regions of the spectrum. Also, it is modular, easy to implement, seamlessly applicable to any convolutional network, and can be trained end-to-end using gradient descent. Our rigorous experiments, performed over benchmark sets and backed up with statistical tests, showed that the deep models equipped with the attention mechanism are competitive with the stateof-the-art band selection techniques and can work orders or magnitude faster, they deliver high-quality classification, and consistently identify significant bands in the training data, permitting the creation of refined and extremely compact sets that retain the most meaningful features. Also, the attention modules do not deteriorate the classification abilities, and slow down neither training nor inference of the deep models.

Distributed Compressed Hyperspectral Sensing Imaging Based on Spectral Unmixing

“…This means that the equivalent sampling rate of the key band SR K is L K /L. Band selection [29][30][31][32][33][34], according to hyperspectral feature, will provide a benefit to the performance of grouping. However, it will violate the requirement of the lowest computational cost at the encoding end of CS.…”

Detection of peach soluble solids based on near‐infrared spectroscopy with High Order Spatial Interaction network

“…As research deepens, the application potential of deep learning methods in feature selection is also increasing, and researchers are beginning to use deep learning networks to extract feature wavelengths 24 . However, researchers often cannot explain which spectral features the model had selected during the training process, nor can they visualize the specific locations of important features in the spectral bands, and the algorithm complexity is high 22,23,25 . Freeborough and Van Zyl 10 used a permutation algorithm (PA) combined with a deep learning network model to determine which feature in financial time series data contributes the most to the model's prediction, overcoming the difficulties of visualization and high algorithm complexity mentioned earlier.…”

Towards On-Board Hyperspectral Satellite Image Segmentation: Understanding Robustness of Deep Learning through Simulating Acquisition Conditions

“…Due to the large volume of hyperspectral imagery, its transfer is time-consuming and costly, so is the manual analysis of newly acquired HSIs. Therefore, deploying automated algorithms for its efficient processing on-board satellites is an important science and engineering topic, and on-board artificial intelligence-employed both in the context of hyperspectral data reduction through band selection [7][8][9] or feature extraction [10], and HSI analysis aiming at extracting the value from raw data-has a potential to speed up adoption of hyperspectral analysis in emerging use cases.…”