Hyperspectral images (HSI) have higher spectral resolution than multispectral images (MSI), but due to limitations of imaging equipment, their width is narrower than MSI. When using partially overlapping HSI-MSI to improve the classification capabilities of MSI, there may be unknown classes that do not exist in HSI-MSI overlapping regions. To solve this problem, this paper proposes a heterogeneous open-set cross-domain manifold embedding aligned method for HSI-MSI collaborative classification. The method designs manifold embedding to align HSI-MSI features to map into subspaces, and gradually selects target domain samples for pseudo-labeling through the designed strategy while rejecting unknown class samples. The feature alignment and pseudo-labeled sample selection are continuously iterated to promote each other, reducing the intra-class distance while pushing the rejected target data away from known classes. The experimental results verify the superiority of our method.

With the development of observation technology, multispectral (MS) images of large scenes are easy to obtain, but the low spectral resolution limits their classification ability. Moreover, the collection of training samples is difficult and time-consuming, and limited labeled samples are a challenge for the precise classification of large-scene MS images. This article attempts to use hyperspectral (HS) images with limited labels to help classify MS images of large scenes, so as to achieve better classification results. To solve this problem, a few-shot MS-HS image collaborative classification method combining feature distribution enhancement (FDE) and subdomain alignment is proposed. Specifically, a residual 3-D convolution network embedded with a 3-D FDE module is designed to improve the diversity of the feature distribution extracted by the network and increase the generalization ability of the model under the few-shot condition. Furthermore, the local domain alignment between the source and target domains is achieved by subdomain alignment, which better aligns the categories in the source domain and the target domain, and achieves the distribution alignment of the subdomains. In addition, the feature bias adjustment (FBA) module is introduced in the test phase to correct the bias of the MS image feature representation, and to alleviate the cross-domain problem to some extent. The few-shot learning (FSL) is applied in the source and target domains to learn better feature mapping. The results of comparative experiments on three datasets show that the proposed method is superior to the most advanced method in the case of limited labeled samples.

Hyperspectral images (HSIs) have higher spectral resolution than multispectral (MS) images, but they have a narrower swath than MS images. The limited spectral resolution of MS images constrains their classification capabilities, and annotating remote sensing data is time-consuming and laborious. In addition, large-scale MS images may contain unknown classes not present in the training data. This article attempts to use partially overlapping HS images with limited labels to assist in the classification of large-scene MS images. It can correctly distinguish known classes and simultaneously identify unknown classes, thereby achieving better classification results for MS images. To address this challenge, a few-shot open-set HS–MS image collaborative classification method is proposed. Specifically, a spectral–spatial feature interactive enhancement (SSFIE) module is designed for richer feature extraction and enhanced classification capabilities in the feature extraction stage. In the few-shot learning (FSL) stage, an adaptive joint similarity metric criterion is proposed to improve feature mapping between the source and target domains. Discriminative joint probability adaptation (DJPA) is used for domain adaptation and to enhance feature discriminability, while batch nuclear-norm maximization (BNM) is employed to increase the feature diversity. In the testing phase, the open-set classification module is designed to correctly classify samples of known classes while simultaneously distinguishing unknown classes. The experimental results on four cross-domain HS–MS data pairs demonstrate that our proposed method outperforms state-of-the-art methods.

The fine spectra of the hyperspectral (HS) images can fully reflect the subtle features of the spectra of different objects. However, due to the limitation of the imaging equipment, its swath is not as large as that of multispectral (MS) images. The acquisition of MS images is more convenient, but the discrimination of spectral features is relatively poor. This paper aims to investigate how partially overlapping HS images can be utilized to improve the classification accuracy of large-scene MS images. Due to the spectral mismatch existing between MS and HS features, traditional transfer learning methods cannot solve the problem of classification with heterogeneous features. To address this issue, a novel structure-preserving discriminative distribution adaptive MS-HS image collaborative classification method is proposed in this paper, which aims to improve the classification accuracy of large-scene MS images by discriminative features. Specifically, this method combines statistical properties and geometric constraints in transfer learning, and jointly maximizes the distance between different classes by discriminative least squares to maximize classification accuracy. Moreover, the source and target domains are probabilistically adaptive while maintaining the local structure of MS-HS features, so that the data distribution is fully aligned and the distance between different classes is increased. The learned mapping matrix enables the mapping of multi-scale spectral-spatial features of MS-HS images to subspaces for classification. Compared with related advanced methods, three sets of MS-HS data sets show that the proposed method can effectively reduce the differences between MS-HS data and achieve better classification results.

With the development of observation technology in remote sensing (RS), large-area multispectral (MS) images can be easily obtained. However, due to the limitation of imaging devices, only a limited range of hyperspectral (HS) images with higher spectral resolution can be obtained. This article mainly focuses on how to use limited HS images to improve the classification performance of MS images. In order to solve this problem, this article proposes an MS–HS image collaborative classification method, which integrates coupled dictionary learning and distance preserved probability distribution. First, image reconstruction based on coupled dictionary learning is performed, in which sparse representation and dictionary learning are used to generate HS images from MS images through spectral superresolution, so that the spectral features of the MS data and HS data are converted to the same feature space for feature space alignment. Second, the probability distribution is adapted, in which the marginal and conditional probabilities are adapted to further narrow the difference between the real HS data and the generated HS data. At the same time, the consistency of the data structure of the source domain before and after the mapping is maintained, so that the same class of data is more compact after the mapping and reduces the spacing within the same class. Compared with the state-of-the-art methods, this article conducts the experiments on three MS–HS RS datasets, which demonstrate the superiority of the proposed method.