Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep learning (DL) techniques have achieved outstanding results in the fields of RSI segmentation and target recognition. In CD research, most of the methods treat multitemporal remote sensing data as one input and directly apply DL-based image segmentation theory on it while ignoring the spatio-temporal information in these images. In this article, a new siamese neural network is designed by combing an attention mechanism (Siamese_AUNet) with UNet to solve the problems of RSICD algorithms. SiameseNet encodes the feature extraction of RSIs by two branches in the siamese network, respectively. The weights are shared between these two branches in siamese networks. Subsequently, an attention mechanism is added to the model in order to improve its detection ability for changed objects. The models are then compared with conventional neural networks using three benchmark datasets. The results show that the Siamese_AUNet newly proposed in this article exhibits better performance than other standard methods when solving problems related to weak CD and noise suppression.

Feature distortions of data are a typical problem in remote sensing image classification, especially in the area of transfer learning. In addition, many transfer learning-based methods only focus on spectral information and fail to utilize spatial information of remote sensing images. To tackle these problems, we propose spectral-spatial weighted kernel manifold embedded distribution alignment (SSWK-MEDA) for remote sensing image classification. The proposed method applies a novel spatial information filter to effectively use similarity between nearby sample pixels and avoid the influence of nonsample pixels. Then, a complex kernel combining spatial kernel and spectral kernel with different weights is constructed to adaptively balance the relative importance of spectral and spatial information of the remote sensing image. Finally, we utilize the geometric structure of features in manifold space to solve the problem of feature distortions of remote sensing data in transfer learning scenarios. SSWK-MEDA provides a novel approach for the combination of transfer learning and remote sensing image characteristics. Extensive experiments have demonstrated that the proposed method is more effective than several state-of-the-art methods.

Deep learning has achieved impressive results on hyperspectral image (HSI) classification. Among them, both convolutional neural networks (CNNs) and graph neural networks (GNNs) have great potential for HSI classification. Supervised CNNs can efficiently extract hierarchical spatial–spectral features of HSIs, but these methods face the problem of high time complexity as the number of network layers increases. Semisupervised GNNs can rapidly capture the structural information of HSIs, while they cannot be well extended to HSI applications because of the process of adjacency matrix consuming large amount of memory resources. In this article, we propose a fast dynamic graph convolutional network and CNN (FDGC) parallel network for HSI classification. We first obtain two classification features by flattening and pooling operations on the results of the convolution layers, which fully exploits the spatial–spectral information contained in the hyperspectral data cube. Then, a dynamic graph convolution module is applied to extract the intrinsic structural information of each patch. Finally, we can obtain the HSI classification results based on these spatial, spectral, and structural features. By using three branches, FDGC can parallelly process multiple features of HSI in a supervised learning manner. In addition, regularization techniques, such as DropBlock and label smoothing, are applied to further improve the generalization capability of the model. Experimental results on three datasets show that our proposed algorithm is comparable with the state-of-the-art supervised learning models in terms of accuracy while also significantly outperforming in terms of training and inference time.

Self-supervised learning has shown remarkable success in image representation learning. Among these methods, masked image modeling and contrastive learning are the most recent and dominant methods. However, these two approaches will behave differently after being transferred into various downstream tasks. In this paper, we propose a RGB-elevation contrastive and image mask prediction pre-training framework. The elevation is normalized digital surface model. Then we evaluate the learned representation by transferring the pre-trained model into change detection task. To this end, we leverage the recently proposed vision transformer’s capability of attending to objects and combine it with the pretext task which is consist of masked image modeling and instance discriminant for fine-tuning the spatial tokens. Besides, the change detection task also requires us to do information interaction between the two temporal remote sensing images. To counter this problem, we propose a plug-in temporal fusion module based on masked cross attention and then we evaluate its effectiveness in three open change detection datasets in terms of initializing the supervised training weights. Our method achieves improvements in comparison to supervised learning methods and two mainstream self-supervised learning methods MoCo and DINO on change detection task. The results of our experiment also achieve state-of-the-art in four change detection datasets. The code will be available at URL.

Hyperspectral images contain abundant spectral information, which provide great potential for target detection. However, it also introduces a critical spectral variability problem for hyperspectral target detection, which makes the hyperspectral target detection much difficult than the classical spectral match issue. Many traditional detection methods have been proposed to deal with the spectral variability. However, these algorithms are still highly susceptible to the target spectral variability. The single input restriction and the inherent spectral characteristics mining problem are the main issues with these methods. The multitask learning (MTL) technique may have the potential to solve the above hyperspectral target detection issues since it can extract the inherent similarity and difference within multiple priori target spectra to learn a robust target spectral signature. This paper proposed a MTL-based reliability analysis method for hyperspectral target detection (MultiRely). This approach: 1) utilizes multiple priori target spectra to better represent the target spectral characteristics and construct multiple related detection tasks; 2) takes full advantage of the multitask learning technique to explore the spectral similarity and difference between multiple priori target spectra; 3) and applies the reliability analysis to obtain a reliable target spectrum in order to alleviate the target spectral variability. Experiments on two real hyperspectral datasets and one synthetic hyperspectral dataset illustrated the effectiveness of the proposed algorithm compared to the state-of-the-art detectors.