Hyperspectral remote sensing (HSRS) images have high dimensionality, and labeling HSRS data is expensive and therefore limited to small amounts of pixels. This makes it challenging to use deep neural networks for HSRS image classification. In extreme cases, deep neural networks are even outperformed by traditional models. In this work, we propose to use Bayesian convolutional neural networks (BCNNs) as a potential alternative to convolutional neural networks (CNNs). BCNNs benefit from Bayesian learning, which is more robust against overfitting and inherently provides a measure for uncertainty. We show in experiments on the Pavia Centre, Salinas, and Botswana datasets that a BCNN outperforms a similarly constructed non-Bayesian CNN, an off-the-shelf random forest (RF), and a state-of-the-art Bayesian neural network (BNN). We also show that BCNN is more robust against overfitting compared with the CNN. Furthermore, the BCNN exhibits a remarkably larger capacity for model compression, which makes BCNN a better candidate in hardware-constrained settings. Finally, we show that the BCNN’s uncertainty measure can effectively identify misclassified samples. This useful property can be used to detect mislabeled data or to reject predictions with low confidence.

Glacier calving front position (CFP) is an important glaciological variable. Traditionally, delineating the CFPs has been carried out manually, which was subjective, tedious, and expensive. Automating this process is crucial for continuously monitoring the evolution and status of glaciers. Recently, deep learning approaches have been investigated for this application. However, the current methods get challenged by a severe class imbalance problem. In this work, we propose to mitigate the class imbalance between the calving front class and the noncalving front class by reformulating the segmentation problem into a pixelwise regression task. A convolutional neural network (CNN) gets optimized to predict the distance values to the glacier front for each pixel in the image. The resulting distance map localizes the CFP and is further postprocessed to extract the calving front line. We propose three postprocessing methods, one method based on statistical thresholding, a second method based on conditional random fields (CRFs), and finally the use of a second U-Net. The experimental results confirm that our approach significantly outperforms the state-of-the-art methods and produces accurate delineation. The second U-Net obtains the best performance results, resulting in an average improvement of about 21% Dice coefficient enhancement.

The vast majority of the outlet glaciers and ice streams of the polar ice sheets end in the ocean. Ice mass loss via calving of the glaciers into the ocean has increased over the last few decades. Information on the temporal variability of the calving front position provides fundamental information on the state of the glacier and ice stream, which can be exploited as calibration and validation data to enhance ice dynamics modeling. To identify the calving front position automatically, deep neural network-based semantic segmentation pipelines can be used to delineate the acquired SAR imagery. However, the extreme class imbalance is highly challenging for the accurate calving front segmentation in these images. Therefore, we propose the use of the Mathews correlation coefficient (MCC) as an early stopping criterion because of its symmetrical properties and its invariance towards class imbalance. Moreover, we propose an improvement to the distance map-based binary cross-entropy (BCE) loss function. The distance map adds context to the loss function about the important regions for segmentation and helps accounting for the imbalanced data. Using Mathews correlation coefficient as early stopping demonstrates an average 15% dice coefficient improvement compared to the commonly used BCE. The modified distance map loss further improves the segmentation performance by another 2%. These results are encouraging as they support the effectiveness of the proposed methods for segmentation problems suffering from extreme class imbalances.

Supervised machine learning requires a large amount of labeled data to achieve proper test results. However, generating accurately labeled segmentation maps on remote sensing imagery, including images from synthetic aperture radar (SAR), is tedious and highly subjective. In this work, we propose to alleviate the issue of limited training data by generating synthetic SAR images with the pix2pix algorithm [1]. This algorithm uses conditional Generative Adversarial Networks (cGANs) to generate an artificial image while preserving the structure of the input. In our case, the input is a segmentation mask, from which a corresponding synthetic SAR image is generated. We present different models, perform a comparative study and demonstrate that this approach synthesizes convincing glaciers in SAR images with promising qualitative and quantitative results.

Fluctuations of the glacier calving front have an important influence over the ice flow of whole glacier systems. It is therefore important to precisely monitor the position of the calving front. However, the manual delineation of SAR images is a difficult, laborious and subjective task. Convolutional neural networks have previously shown promising results in automating the glacier segmentation in SAR images, making them desirable for further exploration of their possibilities. In this work, we propose to compute uncertainty and use it in an Uncertainty Optimization regime as a novel two-stage process. By using dropout as a random sampling layer in a U-Net architecture, we create a probabilistic Bayesian Neural Network. With several forward passes we create a sampling distribution, which can estimate the model uncertainty for each pixel in the segmentation mask. The additional uncertainty map information can serve as a guideline for the experts in the manual annotation of the data. Furthermore, feeding the uncertainty map to the network leads to 95.24 % Dice similarity, which is an overall improvement in the segmentation performance compared to the state-of-the-art deterministic U-Net-based glacier segmentation pipelines.

An essential climate variable to determine the tidewater glacier status is the location of the calving front position and the separation of seasonal variability from long-term trends. Previous studies have proposed deep learning-based methods to semi-automatically delineate the calving fronts of tidewater glaciers. They used U-Net to segment the ice and non-ice regions and extracted the calving fronts in a post-processing step. In this work, we show a method to segment the glacier calving fronts from SAR images in an end-to-end fashion using Attention U-Net. The main objective is to investigate the attention mechanism in this application. Adding attention modules to the state-of-the-art U - N et network lets us analyze the learning process by extracting its attention maps. We use these maps as a tool to search for proper hyperparameters and loss functions in order to generate higher qualitative results. Our proposed attention U-Net performs comparably to the standard U-Net while providing additional insight into those regions on which the network learned to focus more. In the best case, the attention U-Net achieves a 1.5 % better Dice score compared to the canonical U-Net with a glacier front line prediction certainty of up to 237.12 meters.

Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the unaided eye. For example, a common layer combination is red chalk under ink. In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers. In particular, we propose to use two descriptors in hyperspectral historical document analysis, namely hyper-hue and extended multi-attribute profile (EMAP). We show that hyperspectral images enable better layer separation than RGB images, and that spectral focus stacking is an important preprocessing step towards that goal. Our comparative results with other features underline the efficacy of the three proposed improvements.

Labeling data for hyperspectral remote sensing image classification is a tedious and cost-intensive task. As a consequence, it is oftentimes necessary to perform classification when only very limited number of labeled training data is available. Several approaches have been proposed to address this problem. A recent proposal is to generate additional synthetic samples from a Gaussian Mixture Model for each class. One challenge with this approach lies in determining the number of components in the GMM. In this paper, we propose an approximation algorithm to select the number of components, namely Variational Bayesian (VB). The main advantage of VB is that it does not require multiple clustering computations in advance. Variational Bayesian not only greatly decreases the computational cost, but also generates comparable or better results in comparison to other methods.

The amount of training data that is required to train a classifier scales with the dimensionality of the feature data. In hyperspectral remote sensing (HSRS), feature data can potentially become very high dimensional. However, the amount of training data is oftentimes limited. Thus, one of the core challenges in HSRS is how to perform multiclass classification using only relatively few training data points. In this letter, we address this issue by enriching the feature matrix with synthetically generated sample points. These synthetic data are sampled from a Gaussian mixture model (GMM) fitted to each class of the limited training data. Although the true distribution of features may not be perfectly modeled by the fitted GMM, we demonstrate that a moderate augmentation by these synthetic samples can effectively replace a part of the missing training samples. Doing so, the median gain in classification performance is 5% on two datasets. This performance gain is stable for variations in the number of added samples, which makes it easy to apply this method to real-world applications.