Cog-Net: A Cognitive Network for Fine-Grained Ship Classification and Retrieval in Remote Sensing Images

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 article 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 multigrained 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 (VCL). Subsequently, the second stage introduces the VCL module and 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.