For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

Fourier transform imaging spectrometers (FTISs) are widely used in global hyperspectral remote sensing due to the advantages of high stability, high throughput, and high spectral resolution. Spectrum reconstruction (SpecR) is a classic problem of FTISs determining the acquired data quality and application potential. However, the state-of-the-art SpecR algorithms were restricted by the length of maximum optical path difference (MOPD) of FTISs and apodization processing, resulting in a decrease in spectral resolution; thus, the applications of FTISs were limited. In this study, a deep learning SpecR method, which directly learned an end-to-end mapping between the interference/spectrum information with limited MOPD and without apodization processing, was proposed. The mapping was represented as a fully connected U-Net (FCUN) that takes the interference fringes as the input and outputs the highly precise spectral curves. We trained the proposed FCUN model using the real spectra and simulated pulse spectra, as well as the corresponding simulated interference curves, and achieved good results. Additionally, the performance of the proposed FCUN on real interference and spectral datasets was explored. The FCUN could obtain similar spectral values compared with the state-of-the-art fast Fourier transform (FFT)-based method with only 150 and 200 points in the interferograms. The proposed method could be able to enhance the resolution of the reconstructed spectra in the case of insufficient MOPD. Moreover, the FCUN performed well in visual quality using noisy interferograms and gained nearly 70% to 80% relative improvement over FFT for the coefficient of mean relative error (MRE). All the results based on simulated and real satellite datasets showed that the reconstructed spectra of the FCUN were more consistent with the ideal spectrum compared with that of the traditional method, with higher PSNR and lower values of spectral angle (SA) and relative spectral quadratic error (RQE).

Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the “curse of dimensionality”, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ${\ell }_{2,1}-\mathrm{norm}$ and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.

An airborne hyperspectral imaging system is typically equipped on an aircraft or unmanned aerial vehicle (UAV) to capture ground scenes from an overlooking perspective. Due to the rotation of the aircraft or UAV, the same region of land cover may be imaged from different viewing angles. While humans can accurately recognize the same objects from different viewing angles, classification methods based on spectral-spatial features for airborne hyperspectral images exhibit significant errors. The existing methods primarily involve incorporating image or feature rotation angles into the network to improve its accuracy in classifying rotated images. However, these methods introduce additional parameters that need to be manually determined, which may not be optimal for all applications. This paper presents a spectral-spatial attention rotation-invariant classification network for the airborne hyperspectral image to address this issue. The proposed method does not require the introduction of additional rotation angle parameters. There are three modules in the proposed framework: the band selection module, the local spatial feature enhancement module, and the lightweight feature enhancement module. The band selection module suppresses redundant spectral channels, while the local spatial feature enhancement module generates a multi-angle parallel feature encoding network to improve the discrimination of the center pixel. The multi-angle parallel feature encoding network also learns the position relationship between each pixel, thus maintaining rotation invariance. The lightweight feature enhancement module is the last layer of the framework, which enhances important features and suppresses insignificance features. At the same time, a dynamically weighted cross-entropy loss is utilized as the loss function. This loss function adjusts the model’s sensitivity for samples with different categories according to the output in the training epoch. The proposed method is evaluated on five airborne hyperspectral image datasets covering urban and agricultural regions. Compared with other state-of-the-art classification algorithms, the method achieves the best classification accuracy and is capable of effectively extracting rotation-invariant features for urban and rural areas.

Against the background of the ongoing atmospheric warming, the glacial lakes that are nourished and expanded in High Mountain Asia pose growing risks of glacial lake outburst floods (GLOFs) hazards and increasing threats to the downstream areas. Effectively extracting the area and consistently monitoring the dynamics of these lakes are of great significance in predicting and preventing GLOF events. To automatically extract the lake areas, many deep learning (DL) methods capable of capturing the multi-level features of lakes have been proposed in segmentation and classification tasks. However, the portability of these supervised DL methods need to be improved in order to be directly applied to different data sources, as they require laborious effort to collect the labeled lake masks. In this work, we proposed a simple glacial lake extraction model (SimGL) via weakly-supervised contrastive learning to extend and improve the extraction performances in cases that lack the labeled lake masks. In SimGL, a Siamese network was employed to learn similar objects by maximizing the similarity between the input image and its augmentations. Then, a simple Normalized Difference Water Index (NDWI) map was provided as the location cue instead of the labeled lake masks to constrain the model to capture the representations related to the glacial lakes and the segmentations to coincide with the true lake areas. Finally, the experimental results of the glacial lake extraction on the 1540 Landsat-8 image patches showed that our approach, SimGL, offers a competitive effort with some supervised methods (such as Random Forest) and outperforms other unsupervised image segmentation methods in cases that lack true image labels.

For space-based atmospheric wind measurements, full-link simulation is critical for the optimization of the instrument indicators and the evaluation of the measurements’ performance. This paper presents observation simulations and error verification of the mesosphere wind measurement with four emission lines of the O₂(0-1) band by using the space-based Doppler Asymmetric Spatial Heterodyne (DASH), named the Mesosphere Wind Image Interferometer (MWII). The passive wind measurement principle and the DASH concept are first described. The full-link simulation consists of radiation simulation, the instrument forward model, and the wind retrieval model. The four emission lines at about 866.5 nm of the O₂(0-1) band were selected as the observation targets. The radiation characteristics of the target lines were studied and calculated, as well as the background radiation. Based on the LOS radiation integral model, a numerical simulation of the raw observation data was carried out using the instrument model. The interference fringe priority strategy and joint wind decision method were proposed to achieve multiple-emission-line wind retrieval with higher precision. In the simulation, multiple-line retrieval could improve the precision by more than 30% compared to single-line retrieval under the same conditions. The error simulation indicated that the wind profile precision was 3–9 m/s in the altitude range of 50–110 km, with an average accuracy of about 1 m/s, proving that the scheme of MWII has good altitude coverage of the whole mesosphere and a part of the lower thermosphere.