Accurate diagnosis of thyroid nodules using ultrasonography is a highly valuable, but challenging task. With the emergence of artificial intelligence, deep learning based methods can provide assistance to radiologists, whose performance heavily depends on the quantity and quality of training data, but current ultrasound image datasets for thyroid nodule either directly utilize the TI-RADS assessments as labels or are not publicly available. Faced with these issues, an open-access ultrasound image dataset for thyroid nodule detection and classification is proposed, i.e. the TN5000, which comprises 5,000 B-mode ultrasound images of thyroid nodule, as well as complete annotations and biopsy confirmations by expert radiologists. Additionally, the statistical characteristics of this proposed dataset have been analyzed clearly, some baseline methods for the detection and classification of thyroid nodules are recommended as the benchmark, along with their evaluation results. To our best knowledge, TN5000 is the largest open-access ultrasound image dataset of thyroid nodule with professional labeling, and is the first ultrasound image dataset designed both for the thyroid nodule detection and classification. These kinds of images with annotations can contribute to analyze the intrinsic properties of thyroid nodules and to determine the necessity of FNA biopsy, which are crucial in ultrasound diagnosis.

As a convention, satellites and drones are equipped with sensors of both the visible light spectrum and the infrared (IR) spectrum. However, existing remote sensing object detection methods mostly use RGB images captured by the visible light camera while ignoring IR images. Even for algorithms that take RGB-IR image pairs as input, they may fail to extract all potential features in both spectrums. This letter proposes Multispectral DETR, a remote sensing object detector based on the deformable attention mechanism. To enhance multispectral feature extraction and attention, DropSpectrum and SwitchSpectrum methods are further proposed. DropSpectrum facilitates the extraction of multispectral features by requiring the model to detection some of the targets with only one spectrum. SwitchSpectrum eliminates the level bias caused by the fixed order of RGB-IR feature maps and enhances attention on multispectral features. Experiments on the VEDAI dataset show the state-of-the-art performance of Multispectral DETR and the effectiveness of both DropSpectrum and SwitchSpectrum.

To generate a high-spatial-resolution hyperspectral (HHS) image from a high-spatial-resolution multispectral (HMS) image, both spatial information and spectral information should be considered simultaneously if we want to build a more accurate mapping from HMS to HHS. To this end, a spectral and spatial jointed spectral super-resolution method is proposed in this letter using an end-to-end learning strategy for each subspace with the cluster-based multibranch backpropagation neural network (BPNN). More specifically, in addition to the spectra similarity, a modified superpixel segmentation is introduced to jointly take spatial contextual information into account, and a new framework with it is given. Comparisons on the Columbia University Automated Vision Environment (CAVE) data set show that our proposed method outperforms other relative state-of-the-art methods more than 0.3 in the root mean squared error (RMSE) and more than 1.0 in the spectral angle mapper (SAM) index. Especially, an exemplary application is demonstrated using the synchronized observation data collected by the multispectral and hyperspectral sensors mounted on the HJ-1A satellite at the same time.

Spectral library-based spectral super-resolution is an effective but challenging way to obtain high-spatial hyperspectral images (HSIs) from high-spatial multispectral images (MSIs). However, the incomplete spectral coverage of spectral response functions (SRFs) makes it impossible to comprehensively sense the spectral information in the imaging model, thus greatly limits the performance of spectral super-resolution. To deal with this problem, a new spectral library-based spectral super-resolution method under incomplete spectral coverage conditions is proposed in this article. More specifically, a strategy for acquiring a typical set of spectra from the spectral library is proposed, trying to provide spectral observations under incomplete spectral coverage conditions. Second, taking the typical set of spectra and the remaining spectral library as a priori, a new spectral super-resolution model is established under sparse and low-rank constraints. And then, the spectral dictionary is optimized utilizing the spectral information supplied by the prior spectral library. Finally, its corresponding coefficient matrix is optimized using the spatial information supplied by the MSI and the spectral similarity constraint on the typical spectra. Experimental results using different datasets with different SRFs show that our proposed method outperforms other relative state-of-the-art methods in terms of both spectral reconstruction and spatial preservations.

Hyperspectral image classification is mainly based on the spectral information of land covers, but water vapor or Rayleigh scattering will weaken the surface reflectance under the effect of adjacent pixels, and thus lead to the reducing of the discriminative information for the subsequent classification tasks. Atmospheric correction for the weakened bands is one of the most traditional ways to deal with this issue, but as a complete atmospheric correction for both of them is difficult, maybe a systematic exclusion of the severely affected bands base on quantitative evaluation is a better choice. In this paper, an evaluation based weaken band exclusion method for the hyperspectral image classification is proposed, trying to remove the severely affected bands without further atmospheric correction. Specifically, an evaluation model to describe how the water vapor and Rayleigh scattering affect the surface reflectance is constructed, by using the statistical relationship between the radiative transfer model and the band weaken index of spectra among the adjacent pixels. And then, with a simulation experiment, it is shown that water vapor and Rayleigh scattering can really weaken the discriminative information of some specific bands, and the band weaken index can serve as an appropriate index to evaluate the weakening degree of those bands. Finally, on this basis, the total framework of evaluation based weaken band exclusion method is given. The effectiveness and the universality of our proposed method have been verified and compared on four representative tasks of the hyperspectral image classification.

Spectral library based spectral super-resolution from high-spatial multispectral to hyperspectral image is one of the most efficient ways to obtain high-spatial hyperspectral satellite images, which can be used in various applications. Although most of the published universal spectral libraries can really provide reliable spectral information of the common ground objects, poor region specificity of the universal spectral libraries may limit the accuracy of spectral super-resolution and thus the subsequent applications. To address the above issue, this paper proposes a new spectral super-resolution method for high-spatial multispectral satellite images by using the universal and private jointed spectral library. Specifically, a private spectral library consisting of the spectra of interested ground objects is introduced to the universal spectral library, to form a new joint spectral library, and a new spectral super-resolution model by using the joint spectral library is constructed. Then, band matching between the desired high-spatial hyperspectral image and the joint spectral library will be carried out, to map the joint spectral library into a specific spectral library. After that, contributions of the two universal and private spectral dictionaries will be well balanced by a weighting factor under the sparse representation framework. And finally, the spectral dictionary and its related coefficients will be optimized by the alternating direction method of multipliers (ADMM). Comparison results with the relative state-of-the-art methods shown the superiority of this proposed method, and two typical applications of the tobacco and wheat classification will also be given to evaluate its effectiveness in practical applications.