As a significant strategy to handle the small-sample problem in hyperspectral image (HSI) classification, cross-scene transfer learning generally assumes that the effective knowledge is transferred from one source scene to the target scene. However, in reality, there may be some situations where multiple source HSIs are needed, since one source may not cover the complete classes of the target image. In this paper, we propose an incomplete multi-source transfer learning method for HSI classification. In our framework, we first extract spectral-spatial features of source scenes and target scene. Then, we extend the maximum mean discrepancy (MMD) based cross-domain distribution distance to an incomplete multiple sources form, and build a multi-source locality preserved distribution alignment (MLPDA) model to learn transferable features for classification. Experimental results show that the proposed method can effectively transfer discriminative knowledge from several incomplete sources to improve the classification performance of target HSI.

Object detection for high resolution images has been an important cornerstone in remote sensing interpretation. Though considerable success has been made, there still exists issues for hierarchical semantic representation and integration, which limit the performance of existing methods. Therefore, we first analyze different integration patterns of deformable convolution on RetinaNet comprehensively to enhance the adaptability for the geometrical variations elegantly, which may provide the instructions of the future backbone designs for remote sensing images. Second, a feature pyramid network with filter mechanism (F- FPN) is proposed to strengthen the interaction of hierarchical semantics in feature reflow. Experiments on a challenging public dataset DIOR indicate the great performance.

Due to the difficulty and time-consuming of labeling ground truth map in practical situations, unsupervised multiple change detection (MCD) for multispectral images (MSIs) have attracted much attention in recent years. One possible strategy to obtain multiple changes is to assign labels to the binary change result. However, some methods are difficult to obtain the accurate binary result because of the complexity of backgrounds; moreover, assigning labels is also a challenge owing to the limitation of the number of spectral channels in MSIs. Therefore, we propose a novel unsupervised MCD framework based on auto-updating multitemporal matrix factorization (AMMF) and spatiospectral channel augmentation (SSCA). In AMMF, the accurate binary change result can be detected based on joint matrix factorization, during which the distribution and subspace information of each temporal image are regularized to encode the spatiotemporal correlation. In SSCA, some novel augmentation strategies are introduced to increase the number of channels in MSIs to form the normalized high-dimensional maps for each temporal image based on nonlinear operations and convolutional sparse analysis, respectively. MCD can be achieved by integrating the binary change result and directional information that can be calculated by high-dimensional maps of different temporal images. Experiments are conducted on two real MSIs, indicating that the proposed framework performs well in detecting multiple changes.

This letter presents a novel method for hyperspectral image (HSI) restoration, which aims to improve the removal effectiveness of the sparse noise. In contrast to the existing approaches that employ the$L_{1}$-norm for tractable optimization, we apply the non-convex non-smooth$L_{0}$-norm to measure the sparsity of the impulse noise, stripes, deadlines, and other outliers accurately. By combining the low-rank and total variation (TV) priors to exploit the intrinsic properties of the clean HSI and using the patch scheme to preserve local features, the$L_{0}$-PLRTV restoration model is established. In order to deal with the optimization problem, we introduce an equivalent primal-dual formulation to reformulate the$L_{0}$-norm term, and develop a minimization approach for the objective function based on the alternating iterative method. The simulated and real data experiments confirm that the proposed algorithm can effectively reduce the sparse noise in HSI.

With the wide application of deep learning (DL), change detection (CD) for remote-sensing images (RSIs) has realized the leap from the traditional to the intelligent methods. However, many existing methods still need further improvement in practical applications, especially in increasing the effectiveness of feature extraction and reducing the model computational cost. In this article, we propose a novel deep multiscale Siamese network with parallel convolutional structure (PCS) and self-attention (SA) (MSPSNet), which has excellent capabilities of feature extraction and feature integration under an acceptable consumption. It mainly contains three subnetworks: deep multiscale feature extraction, feature integration by the PCS, and feature refinement based on the SA. In the first subnetwork, a deep multiscale Siamese network based on convolutional block is designed to depict the image features at different scales for different temporal images. In the subsequent subnetworks, a PCS model is proposed to integrate multiscale features of different temporal images, and then, an SA model is constructed to further enhance the representation of image information. Experiments are conducted on two public RSI datasets, indicating that the proposed framework performs well in detecting changes.

The combination of the spectral and spatial features is received wide attention in hyperspectral image (HSI) classification. And the multiscale-strategy is an effective way in improving the classification accuracy for HSI due to the various sizes of land covers, which can capture more intrinsic information. For this reason, a multiscale spectral-spatial unified network (MSSN) with two-branch architecture is proposed for hyperspectral image classification. Different from other networks mainly focusing on the multiscale spatial features, the MSSN can jointly extract the multiscale spectral-spatial features, which is based on the reason that features of different layers in CNN correspond to different scales. In the implementation of the MSSN, the 1D CNN and 2D CNN are used to extract the spectral and spatial features respectively. Then the features of the corresponding layers in the two branches will be integrated to the fully-connected layers and finally sent to the classification layers. Experiments on two benchmark HSIs demonstrate that the proposed MSSN can yield a competitive performance compared with other existing methods.

Target detection has become an increasingly important research topic in hyperspectral image (HSI) processing with most existing detecting algorithms focus on the information of required targets while fail to utilize the background of input data. In this paper, we propose a novel target detection method for HSI, which deals with the detection problem from a new contextual perspective. Based on an initial detection result, the background of HSI is extracted by introducing the joint convolutional analysis and synthesis (JCAS) sparse representation method. Then, the initial result is corrected through a background guided approach, false alarm is greatly reduced. Experimental results show that the proposed method performs well in hyperspectral target detection.