Abstract Detecting airborne dust in standard RGB images presents significant challenges. Nevertheless, the monitoring of airborne dust holds substantial potential benefits for climate protection, environmentally sustainable construction, scientific research, and various other fields. To develop an efficient and robust algorithm for airborne dust monitoring, several hurdles have to be addressed. Airborne dust can be opaque or translucent, exhibit considerable variation in density, and possess indistinct boundaries. Moreover, distinguishing dust from other atmospheric phenomena, such as fog or clouds, can be particularly challenging. To meet the demand for a high-performing and reliable method for monitoring airborne dust, we introduce DustNet++, a neural network designed for dust density estimation. DustNet++ leverages feature maps from multiple resolution scales and semantic levels through window and grid attention mechanisms to maintain a sparse, globally effective receptive field with linear complexity. To validate our approach, we benchmark the performance of DustNet++ against existing methods from the domains of crowd counting and monocular depth estimation using the Meteodata airborne dust dataset and the URDE binary dust segmentation dataset. Our findings demonstrate that DustNet++ surpasses comparative methodologies in terms of regression and localization capabilities.

This study compares two machine learning pixel classifiers with classical approaches for detecting camouflage targets in hyperspectral data. Recent applications of machine learning for hyperspectral data exploitation show good land cover classification results. However, the spectral differences between the classes in those studies are usually very high. We evaluate their performance for classifying targets with similar spectra, specifically camouflage objects. The machine learning results are compared to the established ACE and SVM multiclass classifiers. Input parameters for all approaches, such as training data, spectral class references, and background information, are extracted from the same label set in a single flight line. The evaluation is carried out on 15 different datasets of the same area. We evaluate the results on hyperspectral data from an elaborate measurement campaign using a drone-borne HySpex Mjolnir VS-620 using the combined VNIR and SWIR information. The results show that the SVM produces the best overall accuracy in this experiment with highly unbalanced classes. The machine learning approaches PGBS-HSI and SpectralFormer show better results for the classes with fewer samples. The ACE has the best average but lowest overall accuracy among the tested methods. The findings of this study contribute to understanding the strengths and limitations of machine learning and classical approaches for camouflage target detection in hyperspectral data.

This work addresses the challenge of including the spatial dimension into the autoencoder models for lossy compression of different spatially independent and unknown hyperspectral datasets acquired by space-borne hyperspectral sensors. We propose two different 3D-Hybrid Convolutional Autoencoder models with increased compression rates compared to 1D methods that can compress and reconstruct hyperspectral data with arbitrary spectral dimensionality. The architecture of the first 3D-Hybrid model consists of the A1D-CAE in combination with the 2D-CAE. The second 3D-Hybrid model includes the adaptive 1D-CAE and a 3D-CAE. The evaluation of the reconstruction accuracy is measured by comparing the spectral angle and the peak signal-to-noise ratio between the original and the reconstructed data and structural similarity index measure. We show the high transferability and generalizability of our 3D-Hybrid models on different PRISMA datasets. The 3D-Hybrid model is compared with the SSCNet2Dbased on a 2D-CAE and a 3D-CAE model. The findings of this study contribute to understanding the strengths and limitations of machine learning-based compression methods for jointly compressing spectral and spatial information.

This work discusses three individual camouflage experiments from a drone-based hyperspectral measurement campaign conducted in 2021. The experiments were designed to provide insight into different scenarios of camouflage classification and detection of camouflaged objects. The first experiment demonstrates an approach to detect different objects under camouflage using spectral unmixing. The second experiment presents the performance of commonly used hyperspectral classifiers for camouflage detection with respect to natural illumination changes throughout the day. Finally, the third experiment evaluates the effect of moisture on camouflage detection. For all experiments, we discuss the conditions under which hyperspectral data together with established detection and classification approaches can be used to robustly locate camouflage nets, and when detection is impaired.

This work addresses the challenge of transferability of autoencoder models for lossy compression of different spatially independent and unknown hyperspectral datasets acquired from different space sensor platforms. We propose an adaptive 1D convolutional autoencoder architecture that can compress and recover spectral signatures with different numbers of bands. We demonstrate the transferability of the 1D CAE to different sensors by applying different unknown hyperspectral datasets acquired by different sensor platforms. The evaluation of the reconstruction accuracy is measured by comparing the spectral angle and the signal-to-noise ratio between the original and the reconstructed data. We show the high transferability and generalizability of our A1D-CAE model for compression rates cR= 4 on different datasets from the satellite-based PRISMA, DESIS, EnMap and HYPSO-1 sensors. The results show that the proposed A1D-CAE architecture is capable of processing hyperspectral data from multiple sensor sources with different characteristics while achieving high reconstruction accuracy.

HyperThun’22 was a multi-sensor and multi-temporal camouflage detection campaign with drone-carried hyper-spectral, thermal, and RGB instruments. In more than 20 flights, various military targets were imaged with the purpose of analysing detection rates, camouflage transparency, and system performances. This article presents the campaign design, the data processing, and first data insights. Preliminary results show the potential of the acquired data for promising studies.

The growing availability of hyperspectral remote sensing data, specifically from the new hyperspectral satellite missions, requires efficient data compression due to limitations in bandwidth and available storage space while simultaneously preserving the spectral characteristics. Machine learning approaches are a powerful way to address this challenge, but they are usually tailored to only work on one specific sensor. This work addresses the challenge of transferability of autoencoder models for lossy compression of spatially independent and unknown hyperspectral datasets acquired from different sensor platforms. We propose the CompNext1D, an advanced multi-stage adaptive network based on the architecture of the A1D-CAE. The characteristic of the A1D-CAE allows pre-training on a large dataset with wide spectral variability and transferability to other sensor data, e.g., with potentially limited data availability. The compression performance of the CompNext1D is enhanced by using image statistics and shows a high degree of transferability to unknown spectral signatures. We evaluate the reconstruction accuracy with three experiments of increasing complexity. The evaluation is based on the reconstruction accuracy using the SA, SNR, PSNR, and SSIM metrics, and the results are compared to other learning-based lossy compression techniques.We demonstrate the high transferability and generalizability of our A1D-CAE and CompNext1D for compression rates fromcR= 4 tocR≈ 100 on hyperspectral data from different sensors and carrier platforms. The CompNext1D architecture performs well in compressing hyperspectral data from multiple sensor sources with different characteristics while achieving higher reconstruction accuracy compared to state-of-the-art methods.

Hyperspectral target detection experiments under nonideal conditions are scarce. An extensive multi-scale and multi-temporal field experiment was designed towards the goal of knowledge expansion under such circumstances. A range of camouflage materials and specific targets of interest were placed in a realistic natural environment with vegetation cover and varying illumination. In several experiments, aspects like changes in the sun position, variable moisture, and relocations of targets were analysed. Using an aircraft-based and a drone-based imaging spectrometer, the target scenarios were mapped at different daytimes. The data were radiometrically, atmospherically and geometrically processed to allow subsequent data analysis. First insights deliver promising results.