Recent change detection (CD) methods focus on the extraction of deep change semantic features. However, existing methods overlook the fine-grained features and have the poor ability to capture long-range space–time information, which leads to the micro changes missing and the edges of change types smoothing. In this paper, a potential transformer-based semantic change detection (SCD) model, Pyramid-SCDFormer is proposed, which precisely recognizes the small changes and fine edges details of the changes. The SCD model selectively merges different semantic tokens in multi-head self-attention block to obtain multiscale features, which is crucial for extraction information of remote sensing images (RSIs) with multiple changes from different scales. Moreover, we create a well-annotated SCD dataset, Landsat-SCD with unprecedented time series and change types in complex scenarios. Comparing with three Convolutional Neural Network-based, one attention-based, and two transformer-based networks, experimental results demonstrate that the Pyramid-SCDFormer stably outperforms the existing state-of-the-art CD models and obtains an improvement in MIoU/F1 of 1.11/0.76%, 0.57/0.50%, and 8.75/8.59% on the LEVIR-CD, WHU_CD, and Landsat-SCD dataset respectively. For change classes proportion less than 1%, the proposed model improves the MIoU by 7.17–19.53% on Landsat-SCD dataset. The recognition performance for small-scale and fine edges of change types has greatly improved.

Recently, many deep models have been proposed in different fields, such as image classification, object detection and speech recognition. However, most of these architectures require a large amount of training data and employ random initialization. In this paper, we propose to stack feature learning modules for the design of deep architectures. Specifically, marginal Fisher analysis (MFA) is stacked layer by layer for the initialization and we call the constructed deep architecture marginal deep architecture (MDA). When implementing the MDA, the weight matrices of MFA are updated layer by layer, which is a supervised pre-training method and does not need large scale of data. In addition, several deep learning techniques are applied to this architecture, such as back propagation, dropout and denoising, to fine-tune the model. We have compared MDA with some feature learning and deep learning models on several practical applications, such as handwritten digits recognition, speech recognition, historical document understanding and action recognition. Extensive experiments show that the performance of MDA is better than not only shallow feature learning models, but also related deep learning models in these tasks.