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Impact craters, as key geomorphic features on Mars, provide important insights into surface processes and geological evolution. However, automatic classification of crater morphologies remains challenging due to substantial variations in size, degradation degree, and data quality across different types of Martian craters. This study proposes a multi-modal framework for Martian crater classification by integrating infrared imagery, an optical map, and digital elevation model (DEM) data. Specifically, daytime infrared imagery from THEMIS, a color map from the Tianwen-1 MoRIC instrument, and topographic data derived from combined MOLA–HRSC observations are used to capture complementary thermal, morphological, and elevation-related characteristics. A transformer-based feature extraction and cross-modal fusion strategy is adopted, where infrared imagery guides the interaction among multi-source features. Experiments on a carefully constructed dataset covering four crater categories, i.e., standard craters, layered ejecta craters, degraded craters, and secondary craters, demonstrate that the proposed approach achieves an overall precision of 0.848 and a recall of 0.851, outperforming single-modality baselines. Layered ejecta craters exhibit the highest classification performance, benefiting from their distinctive ejecta morphologies, whereas secondary craters remain more difficult to classify due to their small spatial scales. The results highlight the value of multi-modal data for Martian crater morphology classification.

Fusarium head blight (FHB) is a devastating disease of wheat worldwide. In addition to reducing the yield of the crop, the causal pathogens also produce mycotoxins that can contaminate the grain. The development of resistant wheat varieties is one of the best ways to reduce the impact of FHB. To develop such varieties, breeders must expose germplasm lines to the pathogen in the field and assess the disease reaction. Phenotyping breeding materials for resistance to FHB is time-consuming, labor-intensive, and expensive when using conventional protocols. To develop a reliable and cost-effective high throughput phenotyping system for assessing FHB in the field, we focused on developing a method for processing color images of wheat spikes to accurately detect diseased areas using deep learning and image processing techniques. Color images of wheat spikes at the milk stage were collected in a shadow condition and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model detected spikes very accurately in the images since the coefficient of determination for the number of spikes tallied by manual count and the model was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results for spike detection, a new color feature was applied to obtain the gray image of each spike and a modified region-growing algorithm was implemented to segment and detect the diseased areas of each spike. Results showed that the region growing algorithm performed better than the K-means and Otsu’s method in segmenting diseased areas. We demonstrated that deep learning techniques enable accurate detection of FHB in wheat based on color image analysis, and the proposed method can effectively detect spikes and diseased areas, which improves the efficiency of the FHB assessment in the field.

Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from remote sensing images. A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. Graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. Experimental results using the sea ice dataset of the Ross Sea in the Antarctic, produced by Sentinel-2, demonstrate that our GEFU-Net achieves the best performance compared to other commonly used segmentation models. Specifically, it achieves an accuracy of 97.52%, an Intersection over Union of 95.66%, and an F1-Score of 97.78%. Additionally, fewer model parameters and good inference speed are demonstrated, indicating strong potential for practical ice mapping applications.

Chaotic systems, especially hyper-chaotic systems are suitable for digital image encryption because of their complex properties such as pseudo randomness and extreme sensitivity. This paper proposes a new color image encryption algorithm based on a hyper-chaotic system constructed by a tri-valued memristor. The encryption process is based on the structure of permutation-diffusion, and the transmission of key information is realized through hyper-chaotic synchronization technology. In this design, the hash value of the plaintext image is used to generate the initial key the permutation sequence with the Hash table structure based on the hyper-chaotic sequence is used to implement pixel-level and bit-level permutation operations. Hilbert curves combining with the ciphertext feedback mechanism are applied to complete the diffusion operation. A series of experimental analyses have been applied to measure the novel algorithm, and the results show that the scheme has excellent encryption performance and can resist a variety of attacks. This method can be applied in secure image communication fields.

We present “The Wall”, the first web-based platform that animates the Earth in true natural color and close to real-time. The living planet is displayed both during day and night with a pixel resolution of approximately 1 km and a time frequency of 10 min. The automatic processing chains use the synchronized measurements provided by three geostationary satellites: the METEOSAT Second Generation (MSG2), Himawari-8, and GOES-16. A Rayleigh scattering correction is applied, and a cloud of artificial neural networks, chosen to render “true natural color” RBG composites, is used to recreate the missing daytime bands in the visible spectrum. The reconstruction methodology is validated by means of the TERRA/AQUA “Moderate Resolution Imaging Spectroradiometer” (MODIS) instrument reflectance values. “The Wall” is a dynamic broadcasting platform from which the scientific community and the public can trace local and Earth-wide phenomena and assess their impact on the globe.

This work proposes a new methodology to build an Earth-wide mosaic using high-spatial resolution (15 m) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images in pseudo-true color. As ASTER originally misses a blue visible band, we have designed a cloud of artificial neural networks to estimate the ASTER blue reflectance from Level-1 data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same satellite Terra platform. Next, the granules are radiometrically harmonized with a novel color-balancing method and seamlessly blended into a mosaic. We demonstrate that the proposed algorithms are robust enough to process several thousands of scenes acquired under very different temporal, spatial, and atmospheric conditions. Furthermore, the created mosaic fully preserves the ASTER fine structures across the various building steps. The proposed methodology and protocol are modular so that they can easily be adapted to similar sensors with enormous image libraries.

Continuous-spectrum illumination induces severe channel crosstalk and resolution degradation in color imaging systems. To address this issue, this paper introduces near-monochromatic LED illumination matched to the lens design wavelength and proposes a linear crosstalk correction method based on CCD intensity superposition. Experiments under ISO 12233 evaluation reveal that mixed-color LED illumination only enhances the blue channel, while separate monochromatic illumination and image synthesis effectively eliminate crosstalk and boost overall resolution. The proposed linear method calibrates crosstalk coefficients via a standard reference area to correct single-shot mixed-color images, bringing R/B channels close to pure monochromatic performance, with minor over-sharpening in the green channel. This approach provides a feasible linear correction solution to suppress crosstalk and improve resolution for single-shot industrial color imaging with near-monochromatic LED illumination.

Oncosis, depending on DNA damage and mitochondrial swelling, is an important approach for treating cancer and other diseases. However, little is known about the behavior of mitochondria during oncosis, due to the lack of probes for in situ visual illumination of the mitochondrial membrane and mtDNA. Herein, a mitochondrial lipid and mtDNA dual‐labeled probe, MitoMN, and a continuous add‐on assay, are designed to image the dynamic process of mitochondria in conditions that are unobservable with current mitochondrial probes. Meanwhile, the MitoMN can induce oncosis in a light‐activated manner, which results in the enlargement of mitochondria and the death of cancer cells. Using structured illumination microscopy (SIM), MitoMN‐stained mitochondria with a dual‐color response reveals, for the first time, how swelled mitochondria interacts and fuses with each other for a nonlinear enlargement to accelerate oncosis into an irreversible stage. With this sign of irreversible oncosis revealed by MitoMN, oncosis can be segregated into three stages, including before oncosis, initial oncosis, and accelerated oncosis. A mitochondrial lipids and mtDNA dual‐labeling probe with continuous add‐on assay is designed to induce and image the nonlinear enlargement of mitochondria during oncosis. Together with super‐resolution imaging, it is revealed how swelled mitochondria interact and fuse with each other for a nonlinear enlargement to accelerate oncosis into an irreversible stage, which can be a new landmark for staging oncosis process.

Patients with a long history of ulcerative colitis (UC) are at risk of developing a significant complication known as UC‐associated neoplasia (UCAN). To reduce the risk of UCAN and the associated mortality, the current guidelines recommend initiating surveillance colonoscopy 8–10 years after confirmation of UC diagnosis. In recent years, advancements in endoscopic diagnostic technologies, including magnifying and image‐enhancing techniques, have allowed for the production of high‐contrast images that emphasize mucosal structures, vascular patterns, and color tones. Recently, image‐enhanced endoscopy technologies have become available and offer the potential to improve the qualitative endoscopic assessment of UCAN. The use of high‐definition chromoendoscopy enables the evaluation of subtle mucosal patterns in the colon. Magnifying narrow‐band imaging facilitates the visualization of mucosal vascular structures. Texture and color enhancement imaging processes structure, color tone, and brightness aspects more appropriately, whereas linked color imaging optimizes the emphasis on mucosal and vascular redness. Both techniques are expected to excel in the depiction of subtle color variations and mucosal changes characteristic of UCAN. This article provides an overview of the current status and future challenges regarding the use of various image‐enhanced endoscopy techniques in the diagnosis of UCAN.

Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic keratosis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS-DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer.