In the research of hyperspectral image (HSI) classification based on deep learning, the small sample problem and the lack of classification accuracy caused by not considering global information have ...not been well solved. In this paper, an HSI classification method based on dense convolution and conditional random field (DCRF) is proposed. First, the 1D-2D convolution kernel is used to extract the spectral-spatial features and the layers are densely connected to obtain a dense convolutional network to reduce parameters. Second, the Max Pooling layer is used as the output layer of the dense convolutional network to improve the accuracy of feature extraction, and the Softmax layer is used to calculate the probability of the category of the sample and preliminary classification. Finally, the conditional random field is used to fully integrate spatial global information to achieve HSI final classification. Extensive experimental results on two HSI data sets have demonstrated the effectiveness of the proposed DCRF when compared with other state-of-the-art methods.
Taking advantaging of the ability to extract high-level features, the algorithms based on deep learning for hyperspectral imagery (HSI) anomaly detection have drawn great attention in recent years. ...In this paper, we propose a method named spectral-spatial stacked autoencoders based on the bilateral filter (SSSAE-BF). First, the bilateral filter is employed to obtain the derived anomaly components and background components. Second, stacked autoencoders (SAE) are respectively utilized on the derived anomaly component and background component for deep features. Finally, the Reed and Xiaoli detector (RXD) is used on the spectral-spatial features to calculate the detection result. Experiments on two real hyperspectral images demonstrate that the proposed method outperforms the other competitors.
1As a research hotspot, hyperspectral image target detection is more and more widely used in military and civilian fields. In order to make use of the spatial and spectrum information of ...hyperspectral image data at the same time, a new dictionary learning hyperspectral image target detection algorithm based on Tucker tensor decomposition is proposed in this paper. The algorithm uses Tucker tensor decomposition to extract effective local image block spatial spectrum features. A detection model based on sparse representation and collaborative representation is established, and experiments are carried out on two representative hyperspectral images data. From the visual detection results, the algorithm effectively extracts the spatial spectrum features in the complex background and strong noise environment, has a good ability to suppress the background, and the detection target is significant.
Under the constraint of limited training samples, achieving robust tracking of hyperspectral data with various target characteristics using a single network is challenging. To address this issue, ...firstly, we propose a Multi-Band hyperspectral object tracking algorithm based on Spectral Scale-Aware representation (SSAMB). This algorithm can adaptively perceive and capture object characteristics in hyperspectral data across different spectral scales. Secondly, inspired by the "prompt learning" paradigm, we propose a Hyperspectral Object Tracking network based on Spectral Information Prompts (SIPHOT). It aims to fully leverage the powerful tracking capability of the RGB-based model to alleviate the issue of limited training samples affecting the model's generalization performance. Meanwhile, it can effectively utilize the "spectral dimension advantage" of hyperspectral data to enhance object tracking accuracy. The experimental results on the HOT2023 dataset demonstrate that the proposed SSAMB and SIPHOT trackers perform excellently on different spectral data, validating the effectiveness of our methods.
