As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification.

Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U²-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U²-Net. These results indicate that U²-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps.

Remote sensing change detection (CD) involves identifying differences between two satellite images of the same geographic area taken at different times. It plays a critical role in applications such as urban planning and disaster management. Traditional CD methods rely on manually extracted features, which often lack robustness and accuracy in capturing the details of objects. Recently, deep learning-based methods have expanded the applications of CD in high-resolution remote sensing images, yet they struggle to fully utilize the multi-level features extracted by backbone networks, limiting their performance. To address this challenge, we propose a Semantic-Guided Cross-Attention Network (SCANet). It introduces a Hierarchical Semantic-Guided Fusion (HSF) module, which leverages high-level semantic information to guide low-level spatial details through an attention mechanism. Additionally, we design a Cross-Attention Feature Fusion (CAFF) module to establish global correlations between bitemporal images, thereby improving feature interaction. Extensive experiments on the IWHR-data and LEVIR-CD datasets demonstrate that SCANet significantly outperforms existing State-of-the-Art (SOTA) methods. Specifically, the F1-score and the Intersection over Union (IoU) score are improved by 2.002% and 3.297% on the IWHR-data dataset and by 0.761% and 1.276% on the LEVIR-CD dataset, respectively. These results validate the effectiveness of semantic-guided fusion and cross-attention feature interaction, providing new insights for advancing change detection research in high-resolution remote sensing imagery.

Natural hazards are considered to have a strong link with climate change and human activities. With the rapid advancements in remote sensing technology, real-time monitoring and high-resolution remote-sensing images have become increasingly available, which provide precise details about the Earth’s surface and enable prompt updates to support risk identification and management. This paper proposes a new network framework with Transformer architecture and a Residual network for detecting the changes in high-resolution remote-sensing images. The proposed model is trained using remote-sensing images from Shandong and Anhui Provinces of China in 2021 and 2022 while one district in 2023 is used to test the prediction accuracy. The performance of the proposed model is evaluated by using five matrices and further compared to both convention-based and attention-based models. The results demonstrated that the proposed structure integrates the great capability of conventional neural networks for image feature extraction with the ability to obtain global context from the attention mechanism, resulting in significant improvements in balancing positive sample identification while avoiding false positives in complex image change detection. Additionally, a toolkit supporting image preprocessing is developed for practical applications.