Weijia Cao

Chinese Academy of Sciences | CAS · Aerospace Information Research Institute

To enhance security of the bitplane decomposition based image encryption methods, this paper introduces a novel image encryption algorithm using a bitplane of a source image as the security key bitplane to encrypt images. Users have the flexibility to choose (1) any existing or newly generated image as the source image; (2) any decomposition method...

To reduce both the multiplicative complexity and total number of operations, this paper introduces a modeling scheme of the fast Fourier transform (FFT) to decompose the discrete Fourier transform (DFT) matrix recursively into a set of sparse matrices. Integrating three orthogonal transforms, the Hadamard, Modified Haar and Hybrid transforms, the p...

Due to the unpredictability and complexity properties, chaotic maps are widely applied in security, communication, and system control. Existing one-dimensional (1D) chaotic maps can be easily predicted and high-dimensional (HD) ones have more complex structures and higher computation costs. In order to enhance the chaotic performance, this paper pr...

Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex str...

The hyperspectral images (HSIs) classification technique has received widespread attention in the field of remote sensing. However, how to achieve satisfactory classification performance in the presence of a large amount of noise is still a problem worthy of consideration. In this article, a local correntropy matrix (LCEM)-based spatial-spectral fe...

When applied to practical applications, existing chaotic systems exhibit many weaknesses, including discontinuous chaotic intervals and easily predicted chaotic signals. This study proposes an n-dimensional chaotic model (nD-CM) to resolve the weaknesses of existing chaotic systems. nD-CM can produce chaotic maps with any desired dimension utilizin...

Dictionary learning has drawn increasing attention for its impressive performance in obtaining the high-fidelity representations of data and extracting semantics. However, when there exists distribution divergence between source and target data, the representations of target data based on the learned dictionary from source data fail to reveal the i...

With the development of the hyperspectral imaging technique, hyperspectral image (HSI) classification is receiving more and more attention. However, due to high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixels, it is challenging to achieve satisfactory performance for HSI classification. In order to ov...

Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the object’s information in hundreds of spectral bands. However, how to make full advantage of spatial and spectral information from a large number of spectral bands to improve the performance of HSI classification remains an open question. Many HSI classification...

Hyperspectral image (HSI) classification is an important task in earth observation missions. Convolution neural networks (CNNs) with the powerful ability of feature extraction have shown prominence in HSI classification tasks. However, existing CNN-based approaches cannot sufficiently mine the sequence attributes of spectral features, hindering the...

In the field of remote sensing, it is infeasible to collect a large number of labeled samples due to imaging equipment and imaging environment. Few-shot learning (FSL) is the dominant method to alleviate this problem, which pursues quickly adapting to novel categories from a limited number of labeled samples. The few-shot remote sensing scene class...

Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors, which capture information in hundreds of spectral bands of objects. However, how to take full advantage of spatial and spectral information from many spectral bands to improve the performance of HSI classification remains an open question. Many HSI classification works have...

In recent years, few-shot remote sensing scene classification (FSRSSC) has attracted more and more attention. For FSRSSC, most methods currently focus on designing a meta-learning algorithm, which obtains meta-knowledge from limited samples and then applies it to novel tasks. In this work, on the one hand, we optimize the training pipeline of the f...

In recent years, Convolutional Neural Networks (CNNs) have achieved great success in hyperspectral image classification attributed to their unparalleled capacity to extract the local information. However, to successfully learn the high-level semantic image features, they always require massive amounts of manually labeled data during the training pr...

As one of the most important active remote sensing technologies, synthetic aperture radar (SAR) provides advanced advantages of all-day, all-weather, and strong penetration capabilities. Due to its unique electromagnetic spectrum and imaging mechanism, the dimensions of remote sensing data have been considerably expanded. Important for fundamental...

This volume constitutes selected papers presented at the Third International Conference on Computing and Data Science, CONF-CDS 2021, held online in August 2021. The 22 full papers 9 short papers presented in this volume were thoroughly reviewed and selected from the 85 qualified submissions. They are organized in topical sections on advances in...

Synthetic aperture radar (SAR) can provide stable data source for earth observation due to its advantages of all day and night, all-weather, and strong penetration. SAR image classification as a fundamental procedure has been proved its great value in plenty of remote sensing applications. Conventional classification algorithms mainly rely on hand-...

Fine classification of vegetation types has always been the focus and difficulty in the application field of remote sensing. Unmanned Aerial Vehicle (UAV) sensors and platforms have become important data sources in various application fields due to their high spatial resolution and flexibility. Especially, UAV hyperspectral images can play a signif...

This paper presents a medical image encryption algorithm using edge maps derived from a source image. The algorithm is composed by three parts: bit-plane decomposition, generator of random sequence, and permutation. It offers users the following flexibilities: (1) any type of images can be used as the source image; (2) different edge maps can be ge...

Image encryption is an effective approach to protect privacy and security of images. This paper introduces a novel image encryption algorithm using the Truncated P-Fibonacci Bit-planes as security key images to encrypt images. Simulation results and security analysis are provided to show the encryption performance of the proposed algorithm.