Fast and effective responses are required when a natural disaster (e.g., earthquake and hurricane) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of predisaster and postdisaster satellite images, building damage assessment aims at predicting the extent of damage to buildings. With the powerful ability of feature representation, deep neural networks have been successfully applied to building damage assessment. Most existing works simply concatenate predisaster and postdisaster images as input of a deep neural network without considering their correlations. In this article, we propose a novel two-stage convolutional neural network for building damage assessment, called BDANet. In the first stage, a U-Net is used to extract the locations of buildings. Then, the network weights from the first stage are shared in the second stage for building damage assessment. In the second stage, a two-branch multiscale U-Net is employed as the backbone, where predisaster and postdisaster images are fed into the network separately. A cross-directional attention module is proposed to explore the correlations between predisaster and postdisaster images. Moreover, CutMix data augmentation is exploited to tackle the challenge of difficult classes. The proposed method achieves state-of-the-art performance on a large-scale dataset—xBD. The code is available at https://github.com/ShaneShen/BDANet-Building-Damage-Assessment .

Deep-learning-based methods, such as convolution neural network (CNN), have demonstrated their efficiency in hyperspectral image (HSI) classification. These methods can automatically learn spectral-spatial discriminative features within local patches. However, for each pixel in an HSI, it is not only related to its nearby pixels but also has connections to pixels far away from itself. Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient nonlocal module, named ENL-FCN, is proposed for HSI classification. In the proposed framework, a deep FCN considers an entire HSI as input and extracts spectral-spatial information in a local receptive field. The efficient nonlocal module is embedded in the network as a learning unit to capture the long-range contextual information. Different from the traditional nonlocal neural networks, the long-range contextual information is extracted in a specially designed criss-cross path for computation efficiency. Furthermore, using a recurrent operation, each pixel's response is aggregated from all pixels of HSI. The benefits of our proposed ENL-FCN are threefold: 1) the long-range contextual information is incorporated effectively; 2) the efficient module can be freely embedded in a deep neural network in a plug-and-play fashion; and 3) it has much fewer learning parameters and requires less computational resources. The experiments conducted on three popular HSI data sets demonstrate that the proposed method achieves state-of-the-art classification performance with lower computational cost in comparison with several leading deep neural networks for HSI.

Spectral-spatial feature extraction is of great importance to hyperspectral image (HSI) classification. Different from the traditional feature extraction methods, deep learning models such as convolutional neural network (CNN) can learn the spectral-spatial discriminative feature automatically. However, deep learning models usually need to construct a large and complicated network and the training is time-consuming. To deal with these issues, in this paper, a spectral-spatial domain-specific convolutional deep extreme learning machine (ELM), named S2CDELM, is proposed for HSI classification. At first, by using the conception of local receptive filed (LRF), a spectral-spatial convolutional learning module with two branches is constructed for spectral and spatial feature extraction respectively. Specifically, the convolutional learning module is constructed by using random convolutional nodes but without back propagation, in which a spectral branch and a spatial branch are designed respectively. Then the extracted features are concatenated and fed to a fully connected stacked ELM network to further exploit spectral-spatial information for classification. As the convolutional filters and input weights of ELM are randomly generated, the whole framework is compact, simple and fast to construct. Experimental results on popular HSI benchmark data sets demonstrate that S2CDELM can provide satisfactory classification performance and a fast learning speed in comparison with several state-of-the-art classifiers.

Multi-task learning (MTL) for remote sensing image is a rapidly evolving field that requires simultaneous predictions across several related tasks. However, many existing MTL methods often overlook the exploring of cross-task features, while the strong interdependencies among tasks are critical for MTL. In this paper, we propose RSMTMamba, an innovative MTL framework that integrates Mamba for multi-task prediction in remote sensing images. Our network simultaneously performs semantic segmentation, height estimation, and boundary detection within a unified architecture. The proposed architecture prioritizes the decoder, with a shared encoder for feature extraction. Specifically, a Mamba-based Cross-Task Feature Learning (MCFL) module is introduced to capture the interrelations among different tasks. Unlike transformer-based architecture, which requires significant computational resources, the MCFL module can model both local and global cross-task relationships for remote sensing image with linear complexity. Additionally, Mamba-integrated refine decoders are utilized to aggregate features from the encoder, preliminary decoders, and the MCFL module, which enhances multi-task prediction performance. Experimental results on three remote sensing datasets demonstrate that our proposed CFLMamba achieves state-of-the-art prediction performance, outperforming several deep neural networks in remote sensing image analysis. The code is available at https://github.com/sycs2024/RSMultitaskMamba.