Hyperspectral Image (HSI) is used widely in many areas, especially in the remote sensing field. Compared with the traditional remote sensing HSI, the large-scale and high-resolution HSI (LHHSI) which has big data and large size is high-resolution both in spatial domain and spectral domain. However, traditional methods of automatic target detection do not apply to LHHSI. Therefore, this paper proposes a novel framework of automatic target detection for LHHSI based on spatial-spectral interest point (SSIP). It contains five key steps. Firstly, bands selection of LHHSI is used to reduce spectral dimension of LHHSIs. Second, we extract candidate SSIPs from the LHHSIs. Third, we need to determine whether there exist potential target regions by using spectral curves of many selected key SSIPs. And next, the image which contains the potential target regions is divided into image blocks by using quad-tree segmentation, and then every image block is represented by a vector with BoW model based on the selected SSIPs. Finally, these image blocks are classified with SVM. During the classification, if the result is what we need, the quad-tree segmentation of the current block will be ended. The experimental results show that the proposed algorithm has a better performance than traditional algorithms.
Due to the development of hyperspectral image (HSI) technology, the high-resolution hyperspectral image (HRHSI) in remote sensing is becoming widely used. Compared to traditional HSI, HRHSI has extremely high resolution in both spatial and spectral domains. It contains more texture and spectral information than the low-resolution HSI (LRHSI), which can improve the target detection performance of HSI. However, the majority of the existing automatic target detection methods are only applicable to LRHSI. Therefore, this paper brings forward to a spatial-spectral feature-based target detection framework for HRHSI. First, a two-channel residual network is proposed, which aims to learn jointly spatial-spectral features from the spectral domain and spatial domain of HRHSI. Second, a spatial-spectral feature space is constructed to describe the distribution of the spatial-spectral feature of HRHSI, which can overcome the limitation of the number of training samples. A combined loss function is used to minimize within-class differences and maximize between-class distance in the spatial-spectral feature space. Finally, the detection map is received in the spatial-spectral feature space by calculating the Mahalanobis Distance and analysing the credibility of the target. The experimental results show that our algorithm achieves better target detection accuracy when the number of training samples is limited.
Hyperspectral images include richer spectral and spatial information than common images, which are widely used in military, agricultural fields, etc. With the development of sensor technology, the spatial resolution and spectral resolution of hyperspectral images have been improved significantly. However, the disadvantage that there may contain only one part of one object which has different spectral information in hyperspectral images. This will lead to unsatisfactory performance in traditional pixel-level hyperspectral image classification. Thus, a new hyperspectral image classification framework based on convolutional neural network is proposed. First, band selection is adopted to obtain multiple sets of false color images for small sample hyperspectral data. Then, parallel CNNs are introduced to get the classification results of different band combinations. Finally, statistical analysis strategy is performed to obtain the final output result. Experiments show that the classification accuracy of this method is better than that of the previous algorithm on the same dataset.
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