The current paper investigates energy-constraint formation design and analysis problems for multiagent systems with two types of switching interaction topologies; that is, leaderless ones and leader-following ones. Firstly, a new formation control protocol with switching interaction topologies is shown, where the whole energy supply is limited previously. Then, by constructing the relationship of the whole energy supply and the matrix variable, sufficient conditions for leaderless energy-constraint formation design and analysis are respectively presented by the linear matrix inequality tool. Furthermore, an approach is proposed to determine an explicit expression of the formation center function, which determines the macroscopic motion of a multiagent system as a whole and consists of two parts: the initial state term and the formation function term. Moreover, main conclusions of leaderless energy-constraint formation are extended into leader-following multiagent systems by a new two-step transformation approach, where the asymmetric structure feature of leader-following switching interaction topologies is well dealt with. Finally, two numerical examples are provided to demonstrate the effectiveness of main conclusions.

Hyperspectral image (HSI) contains rich spectral information and spatial features, but the huge amount of data often leads to problems of low clustering accuracy and large computational complexity. In this letter, a new clustering method for HSI is proposed, which is named fast spectral embedded clustering based on structured graph learning (FSECSGL). First, the low-dimensional representation of data can be obtained to reduce the scale by the fast spectral embedded method. Then, we use the embedded data to learn an optimal similarity matrix by structured graph learning. Furthermore, the learning structure graph gives feedback to the original bipartite graph to generate better spectral embedded data. As a result, we can obtain a better similarity matrix and clustering result by iteration, which can overcome the limitation of K-means initialization. Experiments show that this method can obtain good clustering performance compared with other methods.

Currently, unsupervised hyperspectral image (HSI) segmentation methods are mainly implemented by clustering. Nevertheless, hyperspectral data contains a large amount of noise during the acquisition process, resulting in an abnormal distribution of many pixel points. Traditional clustering algorithms suffer from inaccurate segmentation when dealing with these data. For example, FCM is sensitive to anomalies in the clustering problem of HSI, that makes the clustering accuracy degraded. To address these problems, this paper proposes a method called Fuzzy C-Multiple-Means (FCMM). The method divides data points with multiple subclusters into defined c clusters. Different from the bottom-up coalescent strategy, the proposed FCMM transforms the problem of merging multiple subclusters into an optimisation problem for the fuzzy affiliation matrix, and updates the partitioning of the q subclusters and c classes by an alternating iterative update method. This enhances the robustness of the algorithm and reduces the effect of outliers in the HSI datasets on the FCMM, which provides superior clustering results. Experiments on several HSI datasets validate the effectiveness of FCMM.

Hyperspectral anomaly detection (HAD) has always been a hot topic in hyperspectral image (HSI) processing. The Reed-Xiaoli (RX) detector is one of the most widely used methods for HAD. However, most improved versions of RX are usually only improved from the view of reducing the contamination of anomaly pixels on the background modeling. Considering the rich spatial information of HSI and the influence of anomaly pixels on background modeling, we propose an ensemble and random RX with multiple features (ERRX MFs) anomaly detector. First, the spectral, Gabor, extended morphological profile (EMP), and extended multiattribute profile (EMAP) four features are extracted. Then, random subsampling is used on RX to obtain multiple detection results for each feature. Finally, the detection results of these four features are further fused to the final detection result by ensemble learning. Experimental results on four real hyperspectral datasets validate that our proposed method can achieve more satisfactory results compared with other state-of-the-art methods.