In recent years, with the rapid development of unmanned aerial vehicles (UAVs) and lightweight hyperspectral imaging (HSI) sensors, mini-UAV-borne hyperspectral remote sensing (HRS) systems have been developed and demonstrate great value and application potential. Compared to spaceborne and airborne HSI systems, mini-UAV-borne HSI systems come with relatively low manufacturing and running costs and have thus become a new research focus in the field of HRS.

Hyperspectral anomaly detection is aimed at detecting observations that differ from their surroundings, and is an active area of research in hyperspectral image processing. Recently, autoencoders (AEs) have been applied in hyperspectral anomaly detection; however, the existing AE-based methods are complicated and involve manual parameter setting and preprocessing and/or postprocessing procedures. In this article, an autonomous hyperspectral anomaly detection network (Auto-AD) is proposed, in which the background is reconstructed by the network and the anomalies appear as reconstruction errors. Specifically, through a fully convolutional AE with skip connections, the background can be reconstructed while the anomalies are difficult to reconstruct, since the anomalies are relatively small compared to the background and have a low probability of occurring in the image. To further suppress the anomaly reconstruction, an adaptive-weighted loss function is designed, where the weights of potential anomalous pixels with large reconstruction errors are reduced during training. As a result, the anomalies have a higher contrast with the background in the map of reconstruction errors. The experimental results obtained on a public airborne data set and two unmanned aerial vehicle-borne hyperspectral data sets confirm the effectiveness of the proposed Auto-AD method.

In recent years, blind source separation (BSS) has received much attention in the hyperspectral unmixing field due to the fact that it allows the simultaneous estimation of both endmembers and fractional abundances. Although great performances can be obtained by the BSS-based unmixing methods, the decomposition results are still unstable and sensitive to noise. Motivated by the first law of geography, some recent studies have revealed that spatial information can lead to an improvement in the decomposition stability. In this paper, the group-structured prior information of hyperspectral images is incorporated into the nonnegative matrix factorization optimization, where the data are organized into spatial groups. Pixels within a local spatial group are expected to share the same sparse structure in the low-rank matrix (abundance). To fully exploit the group structure, image segmentation is introduced to generate the spatial groups. Instead of a predefined group with a regular shape (e.g., a cross or a square window), the spatial groups are adaptively represented by superpixels. Moreover, the spatial group structure and sparsity of the abundance are integrated as a modified mixed-norm regularization to exploit the shared sparse pattern, and to avoid the loss of spatial details within a spatial group. The experimental results obtained with both simulated and real hyperspectral data confirm the high efficiency and precision of the proposed algorithm.

Hyperspectral videos can provide the spatial, spectral, and motion information of targets, which makes it possible to track camouflaged targets that are similar to the background. However, hyperspectral object tracking is a challenging task, due to the huge hyperspectral video data volume and the data hungry problem for the model training. Insufficient training data can seriously interfere with the accuracy and generalization of the tracking models. In this paper, a dual deep Siamese network framework for hyperspectral object tracking (SiamHYPER) is proposed for learning a hyperspectral tracker from a pretrained RGB tracker in the case of the data hungry problem. Specifically, in addition to a pretrained RGB-based Siamese tracker, a hyperspectral target-aware module is designed to mine the spectral information during the target prediction, and a spatial-spectral cross-attention module is introduced to further fuse the deep spectral and spatial features extracted from the RGB tracker and the hyperspectral target-aware module. Benefiting from the guidance training of the RGB tracker, a robust hyperspectral object tracker can be trained effectively with only a small number of hyperspectral video samples, to overcome the data hungry problem. In the experiments conducted in this study, the SiamHYPER framework was verified using SiamBAN and SiamRPN++, with 13 000 frames of hyperspectral video for training, and achieved the best performance on the publicly available hyperspectral dataset released as part of the WHISPERS Hyperspectral Object Tracking Challenge ¹ . The area under the curve (AUC) of SiamHYPER was increased by nearly 8.9% and 7.2%, respectively, when compared with the current state-of-the-art RGB-based and hyperspectral trackers. In addition, the processing speed of SiamHYPER was 19 FPS, which is much higher than that of the current state-of-the-art hyperspectral trackers.

With the maturation of satellite technology, Hyperspectral Remote Sensing (HRS) platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms, which greatly promotes the civil application of HRS imagery in the fields of agriculture, forestry, and environmental monitoring. China is playing an important role in this evolution, especially in recent years, with the successful launch and operation of a series of civil hyperspectral spacecraft and satellites, including the Shenzhou-3 spacecraft, the Gaofen-5 satellite, the SPARK satellite, the Zhuhai-1 satellite network for environmental and resources monitoring, the FengYun series of satellites for meteorological observation, and the Chang’E series of spacecraft for planetary exploration. The Chinese spaceborne HRS platforms have various new characteristics, such as the wide swath width, high spatial resolution, wide spectral range, hyperspectral satellite networks, and microsatellites. This paper focuses on the recent progress in Chinese spaceborne HRS, from the aspects of the typical satellite systems, the data processing, and the applications. In addition, the future development trends of HRS in China are also discussed and analyzed.