In this paper, an efficient and adjustable visual image encryption scheme is proposed by combining a 6D hyperchaotic system, compressive sensing, and Bezier curve embedding. First, the plain image is sparse by discrete wavelet transform (DWT). Then, the sparse image is encrypted and compressed through game-of-life (GOL) hybrid scrambling and compressive sensing into a cipher image. Next, Bezier curve embedding is utilized to embed the cipher image into the carrier image in wavelet domain. After these operations, the final visually meaningful steganographic image is generated. Additionally, the frequency-domain information of the plain image is used to generate the initial values of the 6D hyperchaotic system in scrambling process, which makes the proposed encryption scheme able to effectively resist the chosen-plaintext attacks (CPA) and the knownplaintext attacks (KPA). Moreover, our scheme exhibits excellent adjustable performance compared with existing related schemes. Ultimately, simulation results and comprehensive performance analyses demonstrate that the scheme proposed in this paper has high decryption quality, visual security, robustness, and operating efficiency.

Hyperspectral images (HSIs) consist of hundreds of spectral bands, which can be used to precisely characterize different land cover types. However, an HSI has redundant information and is prone to the "dimensionality curse". Therefore, it is necessary to reduce redundant information through dimensionality reduction (DR), given that different dimensions contain unique primary feature information, and the feature information is complementary. Accordingly, a new feature extraction method based on multi-dimensional spectral regression whitening (M-SRW) is proposed, which reduces HSI to different dimensions and reconstructs it for feature extraction. The proposed method consists of the following steps: First, the original HSI is superpixel segmented by the entropy rate segmentation (ERS) algorithm. Second, spectral regression whitening (SRW) is performed in each superpixel block to reduce the dimension of each superpixel block to a different dimension. Third, superpixel blocks of the same dimension are combined to obtain the reconstructed HSI. Finally, the support vector machine is utilized to classify the reconstructed HSI of different dimensions, and majority voting decision fusion is used to obtain the final classification result map. Experiments on three public hyperspectral data sets demonstrated that the proposed M-SRW method is superior to several state-of-art feature extraction approaches in terms of classification accuracy.

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Convolutional neural networks (CNNs) are widely used for hyperspectral image (HSI) classification due to their better ability to model the local details of HSI. However, CNNs tends to ignore the global information of HSI, and thus lack the ability to establish remote dependencies, which leads to computational cost consumption and remains challenging. To address this problem, we propose an end-to-end Inception Transformer network (IFormer) that can efficiently generate rich feature maps from HSI data and extract high- and low-frequency information from the feature maps. First, spectral features are extracted using batch normalization (BN) and 1D-CNN, while the Ghost Module generates more feature maps via low-cost operations to fully exploit the intrinsic information in HSI features, thus improving the computational speed. Second, the feature maps are transferred to Inception Transformer through a channel splitting mechanism, which effectively learns the combined features of high- and low-frequency information in the feature maps and allows for the flexible modeling of discriminative information scattered in different frequency ranges. Finally, the HSI features are classified via pooling and linear layers. The IFormer algorithm is compared with other mainstream algorithms in experiments on four publicly available hyperspectral datasets, and the results demonstrate that the proposed method algorithm is significantly competitive among the HSI classification algorithms.