Accurate extraction of plastic greenhouses from high-resolution remote sensing imagery is essential for agricultural resource management and facility-based crop monitoring. However, the dense spatial distribution, irregular morphology, and complex background interference of greenhouses often limit the effectiveness of conventional segmentation methods. This study proposes a deep learning framework that integrates a multi-scale Transformer-based decoder with a Swin-UNet architecture to improve feature representation and extraction accuracy. To enhance geometric consistency, a post-processing strategy is introduced, combining connected component analysis and morphological operations to suppress noise and refine boundary shapes. Using GF-2 satellite imagery over Weifang City, China, the model achieved a recall of 92.44%, precision of 91.47%, intersection-over-union of 85.13%, and F1-score of 91.95%. In addition to instance-level extraction, spatial distribution and statistical analysis were performed across administrative divisions, revealing regional disparities in protected agriculture development. The proposed approach offers a practical solution for greenhouse mapping and supports broader applications in land use monitoring, agricultural policy enforcement, and resource inventory.

High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm.

Litchi is an important cash crop in China. Accurate litchi orchard extraction is crucial for agricultural management and economic development. However, litchi orchards are spectrally very similar to evergreen broad-leaved forests and are poorly resolved in medium- and low-resolution satellite imagery, so large-scale extraction remains challenging. A cross-resolution fusion network via semantic anchor-graph enhancement (SAGE-Net) is therefore proposed, using a dual-branch encoder–decoder to aggregate complementary features of AlphaEarth Foundations (AEF) embeddings and GF-1 imagery. To enable semantic interaction across resolutions, the SAGE module is designed to gather the AEF embeddings into a few semantic anchors, reason over the anchor graph, and scatter them back under GF-1 guidance, so that semantic content and spatial extent are supplied by the source better suited to each. To refine the coarse prediction to full resolution without discarding what is already reliable, the reliability-adaptive correction (RAC) head is designed to gate a detail-guided correction by the confidence and local consistency of that prediction, concentrating refinement where it is uncertain. In Shenzhen, SAGE-Net attains the highest IoU (0.5660) and F1-score (0.7229) among eight comparison methods, with a Precision of 0.7162 and a Recall of 0.7297. Under stricter protocols that separate the training and the evaluation patches by a 256 m buffer or by whole spatial blocks, the IoU falls to 0.5224 and 0.5000 and SAGE-Net remains the most accurate of the networks retested, by a smaller margin. The results demonstrate the value of role-aware enhancement and reliability-adaptive correction for fine-grained orchard mapping, and confirm AEF embeddings as an effective supplementary data source for large-scale mapping.

To address the difficulty of finely classifying complex optical remote sensing images, this paper innovatively proposes a new image classification method based on quantum statistics (QS) inspired by quantum physics. Each pixel in the image is regarded as a fermion, which is one of the fundamental particles in quantum systems. The energy of the energy level where fermions are located is described using the negative logarithm of the distribution that the spectrum of the pixel follows. The Fermi-Dirac distribution, a quantum statistics model used to describe the complex occupation pattern of energy levels by fermions, is employed to characterize the membership relationship between pixels and classes, instead of traditional distance measures and probability measures. Then, the cost function guiding the convergence of classification is defined based on free energy, which is used to describe whether a system is in a state of thermal equilibrium according to energy, temperature, and entropy. To minimize the free energy, the derivative method and the simulated annealing algorithm are adopted to estimate the optimal solution for model parameters. The proposed method can describe complex features more effectively, obtain fine classification results, and overcome the curse of dimensionality in high-dimensional image classification. Finally, the feasibility and effectiveness are verified through qualitative and quantitative analysis of multispectral and hyperspectral image classification experiments.