With the wide application of deep learning (DL), change detection (CD) for remote sensing images (RSIs) has realized the leap from the traditional to the intelligent methods. However, many existing methods still need further improvement in practical applications, especially in increasing the effectiveness of feature extraction and reducing model computational cost. In this paper, we propose a novel deep multiscale Siamese network with parallel convolutional structure (PCS) and self-attention (SA) (MSPSNet), which has excellent capabilities of feature extraction and feature integration under an acceptable consumption. It mainly contains three subnetworks: deep multiscale feature extraction, feature integration by the PCS, and feature refinement based on the SA. In the first subnetwork, a deep multiscale Siamese network based on convolutional block is designed to depict image features at different scales for different temporal images. In the subsequent subnetworks, a PCS model is proposed to integrate multiscale features of different temporal images, and then, a SA model is constructed to further enhance the representation of image information. Experiments are conducted on two public RSIs datasets, indicating that the proposed framework performs well in detecting changes.

Tiny object detection in the field of remote sensing has always been a challenging and interesting topic. Despite many researchers have been working on this problem, it has not been well solved due to its complexity. In this paper, we analyze the reasons for the poor performance of deep learning-based object detection methods for tiny objects in remote sensing images. Moreover, we propose a new remote sensing image tiny object detection network based on object reconstruction and multiple receptive field adaptive feature enhancement module (MRFAFEM), called ORFENet. Detailedly, object reconstruction aims to reduce the information loss of tiny objects within deep neural networks, which is only used in the training phase and can be discarded in the inference phase. MRFAFEM is designed to enhance the features for detecting tiny objects by dynamically adjusting the multiple receptive field features. We have conducted several experiments on the AI-TODv2 and LEVIR-Ship datasets, both of which are proposed for tiny object detection in remote sensing images. The experimental results indicate the effectiveness of the proposed method. Specifically, the proposed ORFENet can achieve the AP of 24.8% on the AI-TODv2 dataset and 83.3% AP50 on the LEVIR-Ship dataset. The code will be released at https://github.com/dyl96/ORFENet.

Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method.

The small size of labeled samples has always been one of the great challenges in hyperspectral image (HSI) classification. Recently, cross-scene transfer learning has been developed to solve this problem by utilizing auxiliary samples of a relevant scene. However, the disparity between hyperspectral datasets acquired by different sensors is a tricky problem which is hard to overcome. In this paper, we put forward a cross-scene deep transfer learning method with spectral feature adaptation (SFA) for HSI classification, which transfers the effective contents from source scene to target scene. The proposed framework contains two parts. First, the distribution differences of spectral dimension between source domain and target domain are reduced through a joint probability distribution adaptation approach. Then, a multiscale spectral-spatial unified network (MSSN) with two-branch architecture and a multiscale bank is designed to extract discriminating features of HSI adequately. Finally, classification of the target image is achieved by applying a model-based deep transfer learning strategy. Experiments conducted on several real hyperspectral datasets demonstrate that the proposed approach can explicitly narrow the disparity between HSIs captured by different sensors and yield ideal classification results of the target HSI.

The richness of spectral information in multitemporal hyperspectral images (MHSIs) offers the possibility to effectively detect subtle changes and properties of grounds. However, severe spectral variabilities and inadequate spatial co-exploitation capabilities limit the performance of existing methods due to differences in acquisition times and conditions. Therefore, this article proposes a strategy of joint unmixing and multitemporal spatial information coguidance (JUC) to fully exploit the spatio-temporal-spectral features. First, a multitemporal joint unmixing method is used to achieve endmembers’ extraction and abundance estimation. Wherein the method adds spectral perturbed regularization when compared to the traditional unmixing strategy, making it robust to spectral variability. Second, we propose a multitemporal coguidance method that highlights the contrast between changed and unchanged regions and removes the noise by transferring the common structure information between the multitemporal first principal component map and the abundance difference maps. It will obtain an enhanced abundance difference maps and achieve effective combination of multitemporal spatial information. The final change result can be obtained by combining and thresholding these enhanced abundance difference maps. Experiments on some data sets demonstrate that the proposed algorithm is sufficiently valid and robust for multitemporal images, especially for data containing spectral variabilities and obvious structures.

With the increase in the availability of multitemporal hyperspectral images (HSIs), HSIs change detection (CD) methods, including pixel-level and subpixel-level based methods, have attracted great attention in recent years. However, the widespread presence of mixed pixels in HSIs may make it difficult for pixel-level methods to detect subtle changes; meanwhile, the less utilization of spatial information may also lead to limitations in some subpixel-level methods. Therefore, a joint framework, which aims to combine the advantages of pixel-level in spatial utilization and subpixel-level in temporal and spectral exploration, is proposed to enhance the performance of HSIs CD. Two models, convolutional sparse analysis and temporal spectral unmixing, are introduced and presented to characterize different spatial structures and overcome the effects of spectral variability under this framework, respectively. In addition, a multiple CD-based on subpixel analysis is discussed as well. Experiments conducted on three bitemporal HSIs datasets indicate that the proposed framework is robust in capturing effective features and has achieved great detection accuracy.