The accurate and stable detection of infrared (IR) small targets is essential for long-range monitoring. However, current approaches struggle to effectively bridge the inherent semantic gaps between hierarchical features and emphasize their critical characteristics during multilevel feature fusion. To address this challenge, a new asymmetric intensive interactive fusion network (A$\text {I}^{2}$Net) is proposed to narrow the semantic gap and enhance feature refinement capability in multilevel feature fusion. A$\text {I}^{2}$Net employs a high-resolution network (HRNet) as its backbone, maintaining small target integrity in the network’s deeper layers through a high-resolution feature stream that preserves the information of raw small target. Furthermore, an asymmetric intensive interactive fusion (A$\text {I}^{2}$F) module based on differentiated asymmetric attention weight allocation is proposed to facilitate the interactive fusion of multilevel features and eliminate the semantic gap between hierarchical levels. After that, we develop a multiscale context feature extraction (MCFE) module to enrich spatial detail feature representation across multiple scales. The experimental results on the National University of Defense Technology Single-Frame InfraRed Small Target (NUDT-SIRST) Automatic Target Recognition key laboratory ground/air dataset (ATR ground/air dataset) demonstrate that A$\text {I}^{2}$Net achieves remarkable performance compared to the state-of-the-art methods.

Deep learning (DL) has emerged as a competitive method in single-modality-dominated remote sensing (RS) data classification tasks, but its classification performance inevitably encounters a bottleneck due to the lack of representation diversity in complicated spatial structures with various land cover types. Therefore, the RS community has been actively researching multimodal feature learning techniques for the same scene. However, expert annotation of multisource data consumes a significant amount of time and cost. This article proposes an end-to-end method called semisupervised multimodal dual-path network (SMDN). This method simultaneously explores spatial-spectral features contained in hyperspectral images (HSI) and elevation information provided by light detection and ranging (LiDAR). SMDN exploits an unsupervised novel encoder-decoder structure as the backbone network to construct a multimodal DL architecture by jointly training with a data-specific branch. To obtain discriminative multimodal representations, SMDN is able to guide the collaborative training of two different unsupervised features mapped in the latent subspace with limited labeled training samples. Furthermore, after a simple modification of the fusion strategy in SMDN, it can be applied to unsupervised classification problems. Experimental results on benchmark RS datasets validate the effectiveness of the developed SMDN compared over many state-of-the-art methods.

Hyperspectral image (HSI) classification is a hot topic in the field of remote sensing, and convolutional neural networks (CNNs) have shown good classification performance because of their capabilities of feature extraction. However, traditional CNN-based methods require a lot of labeled data during their training process, although the acquisition of labeled samples is complicated and time-consuming. In addition, a key issue for HSI classification is how to effectively explore the correlation within the spectral dimension and emphasize important spectral bands. In this letter, an end-to-end framework named spectral attention-based self-supervised CNN (SASCNN) is put forward for HSI classification. At first, the SASCNN takes raw 3-D cubes as input data, and a spectral attention module (SAM) is used to adaptively optimize channel-wise characteristics by adjusting the importance among continuous spectral bands. Then, by flexibly adding multilayer concatenation to integrate shallow and abstract features, the designed encoder–decoder part can be used to learn discriminative features and reproduce the inputs in a self-supervised manner. Experiments over the Heihe and Houston datasets demonstrate the effectiveness of the proposed self-supervised learning method.

Recently, the convolutional neural network (CNN) has made great progress in hyperspectral image (HSI) classification because of its powerful feature extraction capability. However, the standard CNN based on grid sampling neglects the inherent relation between HSI data, which leads to poor regional edge delineation and generalization ability. Graph convolutional network (GCN) has been successfully applied to data representation in a non-Euclidean space, and it can extract discriminative embedded features by dynamically updating irregular graphs. In this letter, we propose a novel method termed attention mechanism-based dual-path convolutional network (AMDPCN), which is composed of a GCN-based global information learning model (GILM) and a CNN-based local feature extraction network (LFEN). Specifically, AMDPCN fuses the global spatial relationships explored by GILM and the local discriminant features extracted by LFEN with three different strategies: addition, multiplication, and concatenation. Furthermore, a multi-scale attention mechanism (MS-AM) is developed to mitigate the Hughes phenomenon by adaptive recalibrating the nonlinear interdependence among the features. Experiments on Kennedy Space Center and Indian Pines data sets demonstrate the advantages of the proposed AMDPCN to state-of-the-art methods.

Deep learning-based methods have demonstrated their competitive classification performance with sufficient labeled training samples. However, in practical hyperspectral image (HSI) classification applications, the labeled samples available for training are extremely limited compared with a large amount of unlabeled data, because the expert annotation of HSI is labor-intensive and time-consuming. To address the abovementioned issues, an end-to-end framework called semisupervised spatial–spectral dual-path networks (S3DPN) is proposed to learn discriminative spatial–spectral features from limited labeled data and abundant unlabeled data. Unlike many semisupervised deep learning methods that require to produce pseudo-labels (cluster labels), an unsupervised branch of S3DPN can directly extract deep representations from unlabeled samples, and it utilizes octave convolution (Oct-Conv) to simultaneously mine local detail features and global contextual information of unlabeled samples. S3DPN improves classification results by exploring the fusion features to reconstruct supervised and unsupervised features in turn. Furthermore, a spatial–spectral attention mechanism is employed to take full advantage of supervised features to selectively emphasize effective unsupervised representations and suppress useless ones. Experimental results on three real HSI datasets demonstrate the superior classification performance of the proposed S3DPN compared with many state-of-the-art (SOTA) methods.

Building extraction is a critical part of remote sensing (RS) image interpretation, and it is a popular research topic in the RS community. However, building extraction from RS images is a difficult task due to its various shape, size, and complex scene. The extracted feature of existing deep learning methods is lack of discrimination, resulting in incomplete buildings and irregular boundaries. Most studies are mainly concentrated on urban areas, ignoring illegal blue-roofed building extraction in rural areas. To address the above-mentioned problems, a channel-spatial attention-based encoder-decoder network (CSA-UNet) is proposed for rural blue-roofed building extraction tasks from RS images. To extract the key areas of buildings, the CSA-UNet employed channel-spatial attention to the fused features of encoder and decoder for achieving discriminative and attentive features. At the same time, considering the problem of false negative predictions, a joint loss function is designed by giving weight to positive samples to alleviate this problem and optimize the CSA-UNet model. Furthermore, blue-roofed buildings are a special type of illegal building, so we take blue-roofed buildings as an example to carry out related research. And a blue roof dataset termed UAVBlue is built through unmanned aerial vehicle (UAV). Experimental results exhibit that the CSA-UNet is better than some state-of-the-art (SOTA) methods.

In this letter, a new semisupervised dimensionality reduction (DR) method, termed geodesic-based manifold joint hypergraphs (GMJHs), is proposed for hyperspectral image (HSI). This method first builds a geodesic-based reconstruction model to discover the nonlinear similarity between two manifold reconstruction neighborhoods. Then, it implies the probabilistic relationship between unlabeled samples and each class via the geodesic-based reconstruction distance. With the probabilistic class relationship, a supervised hypergraph and an unsupervised hypergraph are constructed to represent the multivariate manifold relationship of samples. Finally, the supervised and unsupervised hypergraphs are jointed for learning optimal projection matrix and enhancing the intraclass compactness in low-dimensional embedding space. Experiments on two HSI data sets show that the proposed GMJH algorithm performs better performance than some state-of-the-art DR methods.

Deep neural networks have achieved great success in the field of image processing. The feature representation of RGB image can be easily obtained in spatial domain. Different from this, hyperspectral image (HSI) is a kind of high-dimensional data that contains rich spectral information. To explore the manifold structure in HSI, a new deep learning model termed deep manifold learning network (DMLN) was proposed in this paper. In DMLN, a graph based loss function is designed to combine the exploration of manifold structure and the extraction of deep abstract information, which can obtain the discriminant features by iteratively enhancing the compactness of intraclass samples and the separation of interclass samples. Experimental results on two real-world HSI data sets demonstrate the proposed DMLN outperformed some the state-of-the-art methods.

The great success of deep learning in hyperspectral imagery is attributed to the rapidly developing computational resources. Traditional deep learning methods generally use two different frameworks to learn spatial information and spectral information respectively, then stack deep features for classification. In this paper, a 3-D deep learning model named spatial-spectral combination convolutional neural network (SSCCNN) is proposed to extract discriminative spectral-spatial features. SS-CCNN is an end-to-end network, that is, the raw 3-D cubes can be used as input data without any preprocessing. SSCCN-N can learn the spatial-spectral features and combine shallow features and deep features to alleviate the declining -accuracy phenomenon. Experiments on University of Pavia and Indian Pines data set demonstrate SSCCNN can obtain higher classification accuracy than state-of-the-art methods.