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Building segmentation has extensive research value and application prospects in high-resolution remote sensing image (HRSI) processing. However, complex architectural contexts, varied building morphologies, and non-building occlusions make building segmentation challenging. Compared with traditional methods, deep learning-based methods present certain advantages in terms of accuracy and intelligence. At present, the most popular option is to first apply a single neural network to encode an HRSI, then perform a decoding process through up-sampling or using a transposed convolution operation, and then finally obtain the segmented building image with the help of a loss function. Although effective, this approach not only tends to lead to a loss of detail information, but also fails to fully utilize the contextual features. As an alternative, we propose a novel network called NPSFF-Net. First, using an improved pseudo-Siamese network composed of ResNet-34 and ResNet-50, two sets of deep semantic features of buildings are extracted with the support of transfer learning, and four encoded features at different scales are obtained after fusion. Then, information from the deepest encoded feature is enriched using a feature enhancement module, and the resolutions are recovered via the operations of skip connections and transposed convolutions. Finally, the discriminative features of buildings are obtained using the designed feature fusion algorithm, and the optimal segmentation model is obtained by fitting a cross-entropy loss function. Our method obtained intersection-over-union values of 89.45% for the Aerial Imagery Dataset, 71.88% for the Massachusetts Buildings Dataset, and 68.72% for the Satellite Dataset I. [ABSTRACT FROM AUTHOR]

Remote sensing Change Detection (CD) involves identifying changing regions of interest in bi-temporal remote sensing images. CD technology has rapidly developed in recent years through the powerful learning ability of Convolutional Neural Networks (CNN), affording complex feature extraction. However, the local receptive fields in the CNN limit modeling long-range contextual relationships in semantic changes. Therefore, this work explores the great potential of Siamese Transformers in CD tasks and proposes a general CD model entitled STCD that relies on Swin Transformers. In the encoding process, pure Transformers without CNN are used to model the long-range context of semantic tokens, reducing computational overhead and improving model efficiency compared to current methods. During the decoding process, the 3D convolution block obtains the changing features in the time series and generates the predicted change map in the deconvolution layer with axial attention. Extensive experiments on three binary CD datasets and one semantic CD dataset demonstrate that the proposed STCD model outperforms several popular benchmark methods considering performance and the required parameters. Among the STCD variants, the F1-Score of the Base-STCD on the three binary CD datasets LEVIR, DSIFN, and SVCD reached 89.85%, 54.72%, and 93.75%, respectively, and the mF1-Score and mIoU on the semantic CD dataset SECOND were 75.60% and 66.19%. [ABSTRACT FROM AUTHOR]

Land-use scene classification (LUSC) is a key technique in the field of remote sensing imagery (RSI) interpretation. A convolutional neural network (CNN) is widely used for its ability to autonomously and efficiently extract deep semantic feature maps (DSFMs) from large-scale RSI data. However, CNNs cannot accurately extract the rich spatial structure information of RSI, and the key information of RSI is easily lost due to many pooling layers, so it is difficult to ensure the information integrity of the spatial structure feature maps (SSFMs) and DSFMs of RSI with CNNs only for LUSC, which can easily affect the classification performance. To fully utilize the SSFMs and make up for the insufficiency of CNN in capturing the relationship information between the land-use objects of RSI, while reducing the loss of important information, we propose an effective dual-branch hybrid framework, HFCC-Net, for the LUSC task. The CNN in the upper branch extracts multi-scale DSFMs of the same scene using transfer learning techniques; the graph routing-based CapsNet in the lower branch is used to obtain SSFMs from DSFMs in different scales, and element-by-element summation achieves enhanced representations of SSFMs; a newly designed function is used to fuse the top-level DSFMs with SSFMs to generate discriminant feature maps (DFMs); and, finally, the DFMs are fed into classifier. We conducted sufficient experiments using HFCC-Net on four public datasets. The results show that our method has better classification performance compared to some existing CNN-based state-of-the-art methods. [ABSTRACT FROM AUTHOR]

There are only a few low - accuracy methods available for the feature extraction of sparse woods from remote sensing images. Moreover, there is a lack of datasets for intelligent identification. This study proposed a method for intelligent information identification of sparse woods from remote sensing images. First, a dataset was created using QGIS and Python separately to provide data support for model training. Then, feature maps were generated through feature extraction, and then regions of interest (ROIs) were extracted from the feature maps. Subsequently, these ROIs were filtered through pooling operations (ROI align) to reduce the memory consumption caused by too many ROIs in the images. Experiments show that the method proposed in this study can create datasets quickly and facilitate the identification of sparse woods from remote sensing images. Moreover, the Mask R - CNN - based intelligent identification has a target detection mean average precision (MAP) of up to 0.92. [ABSTRACT FROM AUTHOR]