Hyperspectral images with an immense number of spectral bands provide abundant discriminant information for accurate land-cover classification in the remote sensing field. However, these narrow and adjacent bands contain a large amount of redundant information. Analyzing these images always requires a huge storage space with expensive computational costs. Furthermore, their high correlation coefficient would lead to the Hughes phenomenon, hindering the improvement of classification performance. We propose a linear semi-supervised hyperspectral feature extraction method L3ME to learn latent local manifold embeddings. Although labeled samples are beneficial to construct learning models, their number is always limited in real-world tasks. The motivation of this paper is to jointly enhance the contributions of labeled and unlabeled samples for learning local manifold structures of hyperspectral images. Features in labeled samples are extracted by two procedures, the adaptive patch alignment framework and integrated intraclass-interclass relationships, from different perspectives. The former aims to solve the problem of the uneven distribution of classes by introducing spectral angle based adaptive parameters. The latter aims to solve the problem of the uneven distribution of samples by constructing several adjacency graphs. The locality preserving projection is capable of preserving the local neighborhood structure of samples. A penalty for sparse regularization is cleverly integrated into the proposed linear discriminant objective function, which is optimized using a novel updating strategy. The convergence of L3ME is proved in detail and analyzed in this paper. Experiments on three typical hyperspectral datasets illustrate the effectiveness of the proposed method over some state-of-the-art techniques. The implementation of L3ME is available at https://github.com/biowby/L3ME.
This paper proposes a joint image encryption and compression scheme based on a new hyperchaotic system and curvelet transform. A new five-dimensional hyperchaotic system based on the Rabinovich system is presented. By means of the proposed hyperchaotic system, a new pseudorandom key stream generator is constructed. The algorithm adopts diffusion and confusion structure to perform encryption, which is based on the key stream generator and the proposed hyperchaotic system. The key sequence used for image encryption is relation to plain text. By means of the second generation curvelet transform, run-length coding, and Huffman coding, the image data are compressed. The joint operation of compression and encryption in a single process is performed. The security test results indicate the proposed methods have high security and good compression effect.
According to the characters of complex hyperspectral data, sparsity technique is introduced to deep convolutional neural network to handle feature extraction and classification problems. Combining sparse unsupervised learning method with neural network model, it is possible to get a good, sparse representation of the spectral information so that deep CNN model could extract feature information hierarchically and effectively. EPLS algorithm is applied in this paper to combine population sparsity and lifetime sparsity with the advantages of extracting deep feature information of CNN model to get a fine classification model. In the experiment, two hyperspectral data sets are applied for the proposed method, and the results demonstrate fine classification performances of the model.
We propose several methods to transplant the compound chaotic image encryption scheme with permutation based on three-dimensional (3-D) baker onto image formats such as the joint photographic experts group (JPEG) and graphics interchange format (GIF). The new methods avert the discrete cosine transform and quantization, which result in floating point precision loss, and succeed to encrypt and decrypt JPEG images lossless. The ciphered JPEG images generated by our solution own much better randomness than most other existing schemes. Our proposed method for GIF keeps the property of animation successfully. The security test results indicate the proposed methods have high security, and the speed of our algorithm is faster than classical solutions. Since JPEG and GIF image formats are popular contemporarily, we show that the prospect of chaotic image encryption is promising.
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