Nonagriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intraclass differences of cropland changes in high-resolution images. In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, in this article, we propose a CNN-transformer network with multiscale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland CD. In the MSCANet, a CNN-based feature extractor is first utilized to capture hierarchical features, then a transformer-based MSCA is designed to encode and aggregate context information. Finally, a multibranch prediction head with three CNN classifiers is applied to obtain change maps, to enhance the supervision for deep layers. Besides, for the lack of CD dataset with fine-grained cropland change of interest, we also provide a new cropland change detection dataset, which contains 600 pairs of 512 × 512 bi-temporal images with the spatial resolution of 0.5–2m. Comparative experiments with several CD models prove the effectiveness of the MSCANet, with the highest F1 of 64.67% on the high-resolution semantic CD dataset, and of 71.29% on CLCD.

Hyperspectral image (HSI) denoising plays an important role in image quality improvement and related applications. Convolutional neural network (CNN)-based image denoising methods have been predominant due to advances made in the field of deep learning in recent years. Spatial and spectral information are crucial to HIS denoising, along with their correlations. However, existing methods fail to consider the global dependence and correlation between spatial and spectral information. Accordingly, in this article, we propose a novel dual-attention denoising network to overcome these limitations. We design two parallel branches to process the spatial and spectral information separately. The position attention module is applied to the spatial branch to formulate the interdependencies on the feature map, while the channel attention module is applied to the spectral branch to simulate the spectral correlation before the two branches are combined. A multiscale structure is also employed to extract and fuse the multiscale features following the fusion of spatial and spectral information. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively when compared with state-of-the-art methods.

Endmember extraction (EE) plays an important role in the quantitative analysis of hyperspectral images, as the main step in the decomposition of mixed pixels. At present, scholars have proposed many EE algorithms based on the linear spectral mixture model and the convex geometry principle, such as the pixel purity index (PPI) and the vertex component analysis (VCA). At the same time, many intelligent optimization algorithms, such as the particle swarm optimization (PSO) and the discrete PSO (DPSO), have been applied to EE, which can get promising results for real images. However, PSO and DPSO have theoretical limitations and cannot guarantee the global convergence. The problem of premature convergence will reduce the accuracy of the EE result. The quantum-behaved PSO (QPSO) can theoretically guarantee the convergence of the algorithm by combining the quantum mechanics into the PSO. In order to increase the accuracy of the algorithm, this paper proposes an improved QPSO (IQPSO) algorithm for EE. IQPSO has made innovations in population coding and initialization methods. Besides, the collaborative approach for updating the optimal positions of particles can help to solve the difficulties caused by high dimensions. The experimental results show that IQPSO can extract endmembers efficiently and effectively.

Hyperspectral unmixing aims to obtain the hidden constituent materials and the corresponding fractional abundances from mixed pixels, and is an important technique for hyperspectral image (HSI) analysis. In this paper, two characteristics of the abundance variables, namely, the local spatial structural feature and the statistical distribution, are incorporated into nonnegative matrix factorization (NMF) to alleviate the non-convex problem of NMF and enhance the hyperspectral unmixing accuracy. An adaptive local neighborhood weight constraint is proposed for the abundance matrix by taking advantage of the spatial-spectral information of the HSI. The spectral information is utilized to calculate the similarities between pixels, which are taken as the measurement of the smoothness levels. Furthermore, because abrupt changes may appear in transition areas or outliers may exist in spatially neighboring regions, any inappropriate smoothness constraint on these pixels is removed, which can better express the local smoothness characteristic of the abundance variables. In addition, a separation constraint is used to prevent the result from over-smoothing, preserving the inner diversity of the same kind of material. Extensive experiments were carried out on both simulated and real HSIs, confirming the effectiveness of the proposed approach.

Deep Learning (DL) has recently shown considerable potential in the field of Hyperspectral Unmixing (HU), thanks to its advanced capabilities in feature representation. Most joint spectral-spatial DL-based unmixing methods extract spatial features at a single scale, with basic feature fusion techniques. Given the heterogeneity in ground object sizes, these methods, with their singular receptive fields, may capture only incomplete features or blend features from disparate objects, leading to inadequate spatial comprehension. To tackle this challenge, we propose the multiscale aggregation transformer network (MAT-Net), which utilizes an encoder-decoder architecture designed to harness both spectral and spatial data comprehensively. Within this structure, we have devised a dual-stream, multi-branch CNN encoder to extract both spectral and multiscale spatial information. The spatial stream includes three CNN branches with different receptive fields. A block-by-block branching strategy is employed in this stream to maintain the continuity of multiscale spatial information and reduce computational costs. To effectively integrate intricate multiscale features, we introduce a transformer encoder that incorporates multihead self-scale-aggregation attention (MSsaA) blocks. These blocks are designed to adaptively modulate feature weights based on the scale characteristics of the image, enabling a more nuanced feature integration process. Experimental results on synthetic and real datasets demonstrate the effectiveness of MAT-Net.

Due to the difficulty of obtaining labeled data for hyperspectral images (HSIs), cross-scene classification has emerged as a widely adopted approach in the remote sensing community. It involves training a model using labeled data from a source domain (SD) and unlabeled data from a target domain (TD), followed by inference on the TD. However, variations in the reflectance spectrum of the same object between the SD and the TD, as well as differences in the feature distribution of the same land cover class, pose significant challenges to the performance of cross-scene classification. To address this issue, we propose a dual classification head self-training network (DHSNet). This method aligns class-wise features across domains, ensuring that the trained classifier can accurately classify TD data of different classes. We introduce a dual classification head self-training strategy for the first time in the cross-scene HSI classification field and design a self-training loss based on the prediction of the two classification heads. The proposed approach mitigates the domain gap while preventing the accumulation of incorrect pseudo-labels in the model. Additionally, we incorporate a novel central feature attention mechanism to enhance the model’s capacity to learn scene-invariant features across domains. DHSNet significantly outperforms state-of-the-art methods on three cross-scene HSI datasets, achieving 80.23±1.92% OA on the Houston dataset. The code for DHSNet will be available at https://github.com/liurongwhm.