BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. Deep neural networks (DNNs), including autoencoders (AEs), convolutional neural networks (CNNs) and vision transformers (ViTs), have shown remarkable performance in this field due to their powerful ability to model the complicated background. However, for reconstruction tasks, DNNs tend to incorporate both background and anomalies into the estimated background, which is referred to as the identical mapping problem (IMP) and leads to significantly decreased performance. To address this limitation, we propose a model-independent binary mask-guided separation training strategy for DNNs, named BiGSeT. Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image. The background is preserved, while the potential anomalies are suppressed by using an efficient second-order Laplacian of Gaussian (LoG) operator, generating a pure background estimate. In order to maintain separability during training, we periodically update the mask using a robust proportion threshold estimated before the training. In our experiments, We adopt a vanilla AE as the network to validate our training strategy on several real-world datasets. Our results show superior performance compared to some state-of-the-art methods. Specifically, we achieved a 90.67% AUC score on the HyMap Cooke City dataset. Additionally, we applied our training strategy to other deep network structures, achieving improved detection performance compared to their original versions, demonstrating its effective transferability. The code of our method will be available at https://github.com/enter-i-username/BiGSeT.Comment: 13 pages, 13 figures, submitted to IEEE TRANSACTIONS ON IMAGE PROCESSIN

Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models

Deep neural networks (DNNs) have achieved tremendous success in many remote sensing (RS) applications, in which DNNs are vulnerable to adversarial perturbations. Unfortunately, current adversarial defense approaches in RS studies usually suffer from performance fluctuation and unnecessary re-training costs due to the need for prior knowledge of the adversarial perturbations among RS data. To circumvent these challenges, we propose a universal adversarial defense approach in RS imagery (UAD-RS) using pre-trained diffusion models to defend the common DNNs against multiple unknown adversarial attacks. Specifically, the generative diffusion models are first pre-trained on different RS datasets to learn generalized representations in various data domains. After that, a universal adversarial purification framework is developed using the forward and reverse process of the pre-trained diffusion models to purify the perturbations from adversarial samples. Furthermore, an adaptive noise level selection (ANLS) mechanism is built to capture the optimal noise level of the diffusion model that can achieve the best purification results closest to the clean samples according to their Frechet Inception Distance (FID) in deep feature space. As a result, only a single pre-trained diffusion model is needed for the universal purification of adversarial samples on each dataset, which significantly alleviates the re-training efforts and maintains high performance without prior knowledge of the adversarial perturbations. Experiments on four heterogeneous RS datasets regarding scene classification and semantic segmentation verify that UAD-RS outperforms state-of-the-art adversarial purification approaches with a universal defense against seven commonly existing adversarial perturbations. Codes and the pre-trained models are available online (https://github.com/EricYu97/UAD-RS).Comment: Added the GitHub link to the abstrac

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Hyperspectral Modeling of Relative Water Content and Nitrogen Content in Sorghum and Maize

Sorghum and maize are two of the most important cereal grains worldwide. They are important industrially, and also serve as staple crops for millions of people across the world. With climate change, increasing frequencies of droughts, and crops being planted on more marginal land, it is important to breed sorghum and maize cultivars that are tolerant to drought and low fertility soils. However, one of the largest constraints to the breeding process is the cycle time between cultivar development and release. Early evaluation of cultivars with increased the ability to maintain water status under drought and increases nitrogen contents under nitrogen stress could be the key to decreasing breeding cycle time. New tools for non-destructive, high throughput phenotyping are needed to evaluate new cultivars. These new tools can also be used for monitoring and management of crops to improve productivity. Hyperspectral imaging holds promise as one tool to improve the speed and accuracy of predicting numerous plant traits including abiotic stress tolerance characteristics. In this thesis, hyperspectral imaging projects were designed to develop and test prediction models for relative water content (RWC) and nitrogen (N) content of sorghum and maize. The first study utilized three different genotypes of sorghum in an automated hyperspectral imaging system in greenhouses at Purdue University. From this study, models were developed for relative water content and nitrogen content using the data from all three genotypes collectively as well as the data from each genotype individually. Models developed using the spectral and morphological features obtained from the hyperspectral images are predictive of both relative water content and nitrogen content. The coefficients of determination (R2) for all graphs comparing the predicted relative water content to the reference relative water content of sorghum averaged 0.90 while the same graphs for maize averaged 0.64. The coefficients of determination for all graphs comparing the predicted nitrogen content to the reference nitrogen content of sorghum averaged 0.85 while the same graphs for maize averaged 0.61. Models built only with the spectral features for sorghum were also predictive of both relative water content and nitrogen content. The coefficients of determination for all graphs comparing the predicted relative water content to the reference relative water content of sorghum averaged 0.91 while the same graphs for nitrogen content in sorghum averaged 0.85. The nitrogen content models developed using the data from the Tx7000 genotype are highly predictive of both Tx7000 and B35 but not highly predictive of Tx623. However, models developed using the data from Tx623 are highly predictive of all three genotypes. Another important finding from this study was that the water and nitrogen signals overlap and the most predictive models are developed from data where water and nitrogen vary continuously. Models to predict one factor that do not account for variation in the other factor are not very accurate. The second experiment utilized hyperspectral imaging to characterize RWC and N content of maize. Models for RWC and N content were developed using spectral and morphological features. The models developed for maize were not as predictive as the models for sorghum but they were still predictive of RWC and N content for the models developed using all six genotypes and the models developed using the data from the individual genotypes. Models built using the four half-sibling genotypes were not more predictive than the models based on all six genotypes. The final portion of this thesis explored predictions across species using both the sorghum and maize data. We found that models developed using only sorghum were not predictive of the maize reference measurements. However, when the sorghum and maize data were combined and used to generate models, both the RWC model and the N content model were highly predictive for both reference measurements