Effective feature representations play a decisive role in content-based remote sensing image retrieval (CBRSIR). Recently, learning-based features have been widely used in CBRSIR and they show powerful ability of feature representations. In addition, a significant effort has been made to improve learning-based features from the perspective of the network structure. However, these learning-based features are not sufficiently discriminative for CBRSIR. In this paper, we propose two effective schemes for generating discriminative features for CBRSIR. In the first scheme, the attention mechanism and a new attention module are introduced to the Convolutional Neural Networks (CNNs) structure, causing more attention towards salient features, and the suppression of other features. In the second scheme, a multi-task learning network structure is proposed, to force learning-based features to be more discriminative, with inter-class dispersion and intra-class compaction, through penalizing the distances between the feature representations and their corresponding class centers. Then, a new method for constructing more challenging datasets is first used for remote sensing image retrieval, to better validate our schemes. Extensive experiments on challenging datasets are conducted to evaluate the effectiveness of our two schemes, and the comparison of the results demonstrate that our proposed schemes, especially the fusion of the two schemes, can improve the baseline methods by a significant margin.

The content-based remote sensing image retrieval (CBRSIR) has recently become a hot topic due to its wide applications in analysis of remote sensing data. However, since conventional CBRSIR is unsuitable in harsh environments, this paper focuses on the cross-modality CBRSIR (CM-CBRSIR) between synthetic aperture radar (SAR) and optical images. Besides the large inter-class and small intra-class in CBRSIR, CM-CBRSIR is limited by prominent modality discrepancy caused by different imaging mechanisms. To address this limitation, this study proposes a deep cross-modality hashing network (DCMHN). First, we transform optical images with three channels into four different types of single-channel images to increase diversity of the training modalities. This helps the network to mainly focus on extracting the contour and texture shared features, and make it less sensitive to colour information for images across modalities. Secondly, we combine any type of randomly selected transformed images and its corresponding SAR or optical images to form image pairs that are fed into the networks. The training strategy, with paired image data, eliminates the large cross-modality variations caused by different modalities. Finally, the triplet loss, in combination with the hash function, helps the modal to extract the discriminative features of images and upgrade the retrieval efficiency. To further evaluate the proposed modality, we construct a SAR-optical dual-modality remote sensing image dataset (SODMRSID) containing twelve categories. Experimental results demonstrate the superiority of the proposed method with regards to efficiency and generality.

Remote sensing image scene classification (RSISC) is an active task in the remote sensing community and has attracted great attention due to its wide applications. Recently, the deep convolutional neural networks (CNNs)-based methods have witnessed a remarkable breakthrough in performance of remote sensing image scene classification. However, the problem that the feature representation is not discriminative enough still exists, which is mainly caused by the characteristic of inter-class similarity and intra-class diversity. In this paper, we propose an efficient end-to-end local-global-fusion feature extraction (LGFFE) network for a more discriminative feature representation. Specifically, global and local features are extracted from channel and spatial dimensions respectively, based on a high-level feature map from deep CNNs. For the local features, a novel recurrent neural network (RNN)-based attention module is first proposed to capture the spatial layout information and context information across different regions. Gated recurrent units (GRUs) is then exploited to generate the important weight of each region by taking a sequence of features from image patches as input. A reweighed regional feature representation can be obtained by focusing on the key region. Then, the final feature representation can be acquired by fusing the local and global features. The whole process of feature extraction and feature fusion can be trained in an end-to-end manner. Finally, extensive experiments have been conducted on four public and widely used datasets and experimental results show that our method LGFFE outperforms baseline methods and achieves state-of-the-art results.

Nowadays, several remote sensing image capturing technologies are used ranging from unmanned aerial vehicles (UAV) to satellites. Powerful learning-based discriminative features play an essential role in content-based remote sensing image retrieval (CBRSIR). Cross-source CBRSIR (CS-CBRSIR) is used to find relevant remote sensing images across different remote sensing sources (i.e., multispectral images and panchromatic images). But it is limited by large cross-source and intra-source variations caused by different semantic objects, spatial resolution, and spectral resolution. The main limitation of CS-CBRSIR is that it cannot address the inconsistency between different sources and exploit the intrinsic relation between them. This study proposes a discriminative distillation network for CS-CBRSIR to address this limitation. To enlarge the inter-class variations and reduce the intra-class differences, the discriminative features from the first source are first extracted with a well-designed joint optimization configuration (JOC) on the basis of deep neural networks (DNNs). Thereafter, the features extracted from the first source are used as a supervision signal for the second source, feature distribution in common feature space between the first and second sources are made significantly similar. The method proposed in this study simultaneously handles the cross-source and inter-source variations, unlike the existing methods. Extensive experiments on the DSRSID dataset with Euclidean distance verify the effectiveness of our proposed method.method is superior to the existing CS-CBRSIR approaches.

In light of the escalating volume of high-quality remote sensing ship images, the imperative task is to effectively classify and retrieve such images from extensive remote sensing archives. While prior research has yielded promising outcomes in ship classification, a comprehensive framework catering to both ship classification and retrieval remains absent. Additionally, prevailing studies neglect the critical aspect of model interpretability, merely furnishing predicted results devoid of a transparent reasoning process. This opacity, coupled with the high-stakes nature of outcomes, significantly impedes the safe utilization of these models. To address these problems, this paper introduces the cognitive network (Cog-Net), an inherently interpretable model tailored for fine-grained ship classification and retrieval in remote sensing images. Cog-Net imitates the reasoning process employed by domain experts, navigating from perception to cognition during decision-making. The initial stage incorporates the Causal Multi-grained Feature Learning (CMFL) module, mirroring the human perceptual process by identifying salient regions of a ship object within entire images as references for visual concept learning. Subsequently, the second stage introduces the Visual Concept Learning (VCL) module, imitates the human cognitive process by learning basis visual concepts for each ship category and generating predictions through interpretable reasoning based on these basis visual concepts. Furthermore, to facilitate experimentation, a novel dataset, Fine-grained Ship Remote Sensing Image Slices (FGSRSI-23), comprising 23 fine-grained ship subcategories, is constructed. Extensive experiments are conducted, encompassing our FGSRSI-23 dataset alongside two publicly available datasets, FGSC-23 and FGSCR-42. Results attest to the competitiveness of Cog-Net in both ship image classification and retrieval tasks, offering a transparent and interpretable reasoning process for predicted outcomes.