Wei Zhang

Fine classification of complex agricultural landscapes is essential for precise agricultural information acquisition. Hyperspectral imagery (HSI) combined with convolutional neural networks (CNNs) offers an effective means for land cover classification. However, conventional CNNs are built on scalar neurons, which limits their ability to effectively filter and capture joint spectral–spatial information in complex scenes. To overcome these limitations, this study proposes the Agricultural Capsule Network (AgriCapsNet), an encoder-decoder capsule network designed for multi-crop agricultural landscape classification with HSI. The encoder integrates a feature-focused former module, comprising lightweight convolution and attention mechanisms, to enhance feature refinement and extraction, together with capsule layers that leverage vector neurons to strengthen hierarchical feature representation. The decoder employs a joint margin and reconstruction loss to improve classification generalization. In addition, a new hyperspectral dataset, Zhang-Jia-Gang, is publicly released to support research on fine-grained agricultural landscape classification. AgriCapsNet was evaluated on three crop-rich HSI datasets and outperformed nine competing models, achieving overall accuracy improvements of 0.90%–24.49%, 1.87%–28.82%, and 1.84%–7.09%, respectively. It also demonstrated strong robustness to noise and adaptability to different pixel neighborhood sizes, confirming its suitability for large-scale agricultural monitoring.

1. The continuous growth of population, rapid urbanization, and mounting pressure on resource and environmental carrying capacities have directly or indirectly influenced crop types, yields, and sp...

Coal, as a traditional energy source, has made remarkable contributions to global economic development. However, surface coal mining brings a series of eco-environmental problems. Therefore, it is crucial to obtain the distribution information of coal mines. Due to the diverse appearance of coal mines and complex background environments, it is very challenging to identify coal mines at a large scale. Exposed coal is an important indicator of coal mining. Spectral indices based on satellite images possess the advantages of simplicity and high efficiency. In this study, the Exposed Coal Index (ECI) was proposed. It enables the accurate identification of exposed coal at a large scale. The effectiveness of the ECI was investigated in four typical surface coal mine distribution regions across the world. Through spectral analysis, two key characteristics of coal spectra were discovered (i.e., the flat spectral shape in the visible to near-infrared range and the low reflectance in the near-infrared band). The ECI utilized these two features to successfully differentiate coal from various background land cover types in all study cases. The results showed that the ECI was effective in visual evaluation, separability analysis, and coal mapping, with superior performance than the three previously proposed indices. The ECI can also be perfectly applied to Landsat 8 images, demonstrating its excellent generalization capability. In addition, compared with three global mining datasets, ECI provided more comprehensive information on coal mine distribution. The proposed ECI is simple, robust, and expected to provide strong support for regional resource management and sustainable development.

Scene change detection identifies functional changes at the scene level. Compared with pixel-level and object-level change detection, it can provide a higher level understanding of changes on the Earth’s surface. Triple-branch networks that perform scene binary change detection and scene classification tasks simultaneously are competitive in the field of scene change detection, as they consider both single-temporal scene semantic information and cross-temporal change features. However, some problems still exist. First, the temporal change feature extraction is insufficient, and the 1-D feature vector used for scene change detection and classification is lacking in representativeness. Second, the predicted scene binary change detection and classification results are often contradictory at the network prediction stage, leading to the unsatisfactory performance of change trajectory identification. To address these issues, a novel framework that integrates differential aggregation network (DAN) and class probability-based fusion strategy (CPFS) was proposed. The designed DAN can fully capture the temporal change features by using four advanced differential fusion modules to aggregate the multi-level difference information. In addition, it is able to generate more representative 1-D feature vectors by adopting two novel attention-aware adaptive pooling modules. The developed CPFS produces the final consistent scene binary change detection and classification maps by fusing three predicted class probability vectors. The proposed method was validated on two datasets, and the results demonstrated its superiority to the comparison methods.

Accurate cropland mapping is significant for food security and sustainable development. The existing cropland map based on remote sensing mainly focus on moderate to coarse spatial resolution, and these products are generally unsuitable for precision agriculture due to the lack of spatial details. Therefore, there is an urgent need to produce high-resolution (HR) cropland maps to meet current application demands. Recently, the typical classification workflow of HR images employs deep learning models combined with manually annotated samples, and visual interpretation of samples is usually labor-intensive and time-consuming, which is not conducive to large-scale applications. To address this problem, this paper proposes an automated HR cropland extraction solution, namely RRE (Refinement-Reclassification-Extraction), including (i) Refinement of 10 m spatial resolution cropland products, (ii) Reclassifying cropland using the refined product as sample source, and (iii) Extracting HR cropland via designed cross-scale sample transfer. The strength of the proposed framework is that it leverages existing moderate-resolution public products as prior knowledge and provides cross-scale transferable samples for HR images. The whole process does not require manual labeling of samples and is highly automated. Specifically, the experimental results in the three main grain production regions show that, the RRE framework effectively reduces the interference of road networks and ridges, and F1 scores of extracted 1 m HR cropland reaches 87.71 %~94.16 %, which is comparable to the fully supervised cropland extraction method. In addition, the 10 m reclassified cropland, produced by the intermediate process of the RRE, outperforms current cropland product of ESRI Land Cover and ESA World Cover.

Accurate and timely information on urban vegetation (UV) can be used as an important indicator to estimate the health of cities. Due to the low cost of RGB cameras, true color imagery (TCI) has been widely used for high spatial resolution UV mapping. However, the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples, respectively. To address this issue, an index-guided semantic segmentation (IGSS) framework is proposed in this paper. Firstly, a novel cross-scale vegetation index (CSVI) is calculated by the combination of TCI and Sentinel-2 images, and the index value can be used to provide an initial UV map. Secondly, reliable UV and non-UV samples are automatically generated for training the semantic segmentation model, and then the refined UV map can be produced. The experimental results show that the proposed CSVI outperformed the existing five RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds, and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72% ∼ 88.16% and an F1 score of 87.73% ∼ 88.37%, which is comparable with the fully-supervised method.