Hyperspectral images (HSI) contain powerful spectral characterization capabilities and are widely used especially for classification applications. However, the rich spectrum contained in HSI also increases the difficulty of extracting useful information, which makes the feature extraction method significant as it enables effective expression and utilization of the spectrum. Traditional HSI feature extraction methods design spectral features manually, which is likely to be limited by the complex spectral information within HSI. Recently, data-driven methods, especially the use of convolutional neural networks (CNNs), have shown great improvements in performance when processing image data owing to their powerful automatic feature learning and extraction abilities and are also widely used for HSI feature extraction and classification. The CNN extracts features based on the convolution operation. Nevertheless, the local perception of the convolution operation makes CNN focus on the local spectral features (LSF) and weakens the description of features between long-distance spectral ranges, which will be referred to as global spectral features (GSF) in this study. LSF and GSF describe the spectral features from two different perspectives and are both essential for determining the spectrum. Thus, in this study, a local-global spectral feature (LGSF) extraction and optimization method is proposed to jointly consider the LSF and GSF for HSI classification. To increase the relationship between spectra and the possibility to obtain features with more forms, we first transformed the 1D spectral vector into a 2D spectral image. Based on the spectral image, the local spectral feature extraction module (LSFEM) and the global spectral feature extraction module (GSFEM) are proposed to automatically extract the LGSF. The loss function for spectral feature optimization is proposed to optimize the LGSF and obtain improved class separability inspired by contrastive learning. We further enhanced the LGSF by introducing spatial relation and designed a CNN constructed using dilated convolution for classification. The proposed method was evaluated on four widely used HSI datasets, and the results highlighted its comprehensive utilization of spectral information as well as its effectiveness in HSI classification.

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products.

In the interest of national security, certain geographical targets such as military installations, nuclear facilities, and secret agencies must be concealed in satellite images prior to public release. This involves the modification of specific regions within the satellite image through techniques such as pixelation, replacement, and blurring to obscure details of sensitive targets. However, the technical process may not completely eliminate all traces of the sensitive information, potentially enabling a satellite imagery analyst to identify the concealed targets. This paper presents an automatic detection method for identifying blurred areas, which may correspond to sensitive targets, within remote sensing images. The experimental results demonstrate that this method effectively detects blurred areas with minimal omissions. Furthermore, this paper explores the relationship between the level of visual blurring and the difficulty of automatic detection, emphasizing the importance of selecting an appropriate blur level for images with varying degrees of heterogeneity.