Semantic Change Detection (SCD) is recognized as both a crucial and challenging task in the field of image analysis. Traditional methods for SCD have predominantly relied on the comparison of image pairs. However, this approach is significantly hindered by substantial imaging differences, which arise due to variations in shooting times, atmospheric conditions, and angles. Such discrepancies lead to two primary issues: the under-detection of minor yet significant changes, and the generation of false alarms due to temporal variances. These factors often result in unchanged objects appearing markedly different in multi-temporal images. In response to these challenges, the MapChange framework has been developed. This framework introduces a novel paradigm that synergizes temporal-invariant historical map data with contemporary high-resolution images. By employing this combination, the temporal variance inherent in conventional image pair comparisons is effectively mitigated. The efficacy of the MapChange framework has been empirically validated through comprehensive testing on two public datasets. These tests have demonstrated the framework's marked superiority over existing state-of-the-art SCD methods.

The advent of high-resolution satellite technology has significantly improved the accuracy of land cover classification, but it also presents new challenges. Deep learning methods, particularly Convolutional Neural Networks (CNNs) and Transformers, have shown promise in overcoming these challenges, yet they are highly reliant on extensive annotated datasets. An alternative, using lower resolution land cover products for label generation, introduces Label Noise, which can adversely affect the learning process and model performance. To address the lack of quality labels and the Label Noise problem, this study introduces the LabelAgent framework. This framework combines high-resolution, recent remote sensing data with historical, low-resolution product data to produce higher quality training labels. Furthermore, it employs a deep reinforcement learning-based approach to intelligently identify and mitigate noisy labels, thereby stabilizing the training process and enhancing the classification efficacy. The outcome of this research offers a novel solution to the challenges in remote sensing image classification, promising improved accuracy and generalization capability for land cover classification models.

In remote sensing imagery analysis, patch-based methods have limitations in capturing information beyond the sliding window. This shortcoming poses a significant challenge in processing complex and variable geo-objects, which results in semantic inconsistency in segmentation results. To address this challenge, we propose a dynamic scale perception framework, named GeoAgent, which adaptively captures appropriate scale context information outside the image patch based on the different geo-objects. In GeoAgent, each image patch’s states are represented by a global thumbnail and a location mask. The global thumbnail provides context beyond the patch, and the location mask guides the perceived spatial relationships. The scale-selection actions are performed through a Scale Control Agent (SCA). A feature indexing module is proposed to enhance the ability of the agent to distinguish the current image patch’s location. The action switches the patch scale and context branch of a dual-branch segmentation network that extracts and fuses the features of multi-scale patches. The GeoAgent adjusts the network parameters to perform the appropriate scale-selection action based on the reward received for the selected scale. The experimental results, using two publicly available datasets and our newly constructed dataset WUSU, demonstrate that GeoAgent outperforms previous segmentation methods, particularly for large-scale mapping applications.

Wetlands are among the most valuable environmental resources and are crucial to achieving sustainable development strategies. However, the number, distribution, and types of wetlands are poorly understood. Widely used medium-resolution wetland products do not provide enough surface feature for fine-grained wetland mapping. To fill this gap, a high-resolution wetland remote sensing mapping dataset covering the contiguous United States was constructed. On this dataset, the performance of deep semantic segmentation networks with various backbones and architecture for wetland mapping was evaluated. Several loss functions were implemented to address the issue of class imbalance of this dataset, resulting in an improvement in classification accuracy. High spatial resolution images offer an abundance of surface texture, shape, structure, and neighborhood relationship data that can be applied to the classification of large-scale wetlands. The combination of high-resolution imagery and deep semantic segmentation models enables the automatic classification of wetlands to be refined.

In this paper, a new semantic change detection (CD) dataset based on Chinese Gaofen-2 (GF-2) satellite images with high spatial resolution (HSR) namely Wuhan Urban Semantic Understanding (WUSU) dataset is built up and a CD framework combining binary and semantic CD tasks based on deep learning and a conditional random field model (SDCRF) is proposed. Existing CD datasets mostly focus on “change/no change”. Traditional CD methods pay attention only on either of the binary CD task or the semantic CD task. Although there are methods to handle both tasks simultaneously but they ignore the inconsistency between the two tasks. In the SDCRF framework, any state-of-the-art feature extraction model can be used to extract the class and change probabilities as the unary potential of a fully connected conditional random field (FC-CRF) model which is adopted as a post-processing to enhance the location information of deep networks and reduce outlier noise.

High spatial resolution (HSR) remote sensing images can reflect more subtle changes and more specific types of land use and land cover (LULC) due to the abundant spatial geometric information. In this article, a class-prior object-oriented conditional random field (COCRF) framework consisting of a binary change detection (CD) task and a multiclass CD task is proposed to fill the application gap. In the proposed framework, the class-prior knowledge is used to improve the construction of the unary potential in both the binary and multiclass CD tasks, to reduce the influence of spectral variability. The binary CD result provides a constraint to the multiclass CD result. As a result, both parts have effective interaction. The class posterior probability images of two dates can be obtained automatically with the class-prior knowledge by sample migration. Furthermore, an object constraint described by the class dispersion within the objects is added to improve the smoothness in local objects, while the pairwise potential improves the smoothness of the whole area by using the eight-neighborhood spectral information of the center pixel. By integrating the above approaches, the problems of error accumulation and the manual intervention required in the traditional multiclass CD methods can be relieved. An adaptive parameter estimation strategy is also adopted in the proposed framework, to save the time required for manual parameter setting. The proposed COCRF framework was validated on two HSR remote sensing image data sets, where it achieved a better performance than the other state-of-the-art CD methods.