Convolutional neural networks (CNN)-based salient object detection (SOD) models have achieved promising performance in optical remote sensing images (ORSIs) in recent years. However, the restriction concerning the local sliding window operation of CNN has caused many existing CNN-based ORSI SOD models to still struggle with learning long-range relationships. To this end, a novel transformer framework is proposed for ORSI SOD which is inspired by the powerful global dependency relationships of transformer networks. This is the first attempt to explore global and local details using transformer architecture for SOD in ORSIs. Concretely, we design an adaptive spatial tokenization transformer encoder to extract global-local features, which can accurately sparsify tokens for each input image and achieve competitive performance in ORSI SOD tasks. Then, a specific dense token aggregation decoder (DTAD) is proposed to generate saliency results, including three cascade decoders to integrate the global-local tokens and contextual dependencies. Extensive experiments indicate that the proposed model greatly surpasses twenty state-of-the-art (SOTA) SOD approaches on two standard ORSI SOD datasets under seven evaluation metrics. We also report a comparison results to demonstrate the generalization capacity on the latest challenging ORSI datasets. In addition, we validate the contributions of different modules through a series of ablation analyses, especially the proposed adaptive spatial tokenization module (ASTM), which can halve the computational budget.

In the multifaceted marine environment, numerous factors affect the noise radiated by ships, thereby weakening the traditional spectral characteristics and diminishing the spectrum’s ability to express identity information. Consequently, these changes result in reduced accuracy in recognition tasks. To address this issue, we propose a novel method for underwater acoustic target recognition aimed at extracting the intrinsic frequency distribution features of different frequency band energies in ship radiated noise to supplement traditional time-frequency features, named ISNet. This method iteratively extracts the real and imaginary parts of the frequency features using Short-Time Fourier Transform (STFT) and combines them with time-frequency features to form a feature matrix. After enhancement through fuzzy mixed features, the features are input into a dimensionality-reduced RSNet-18 network for training, and final predictions are obtained through a step-wise voting strategy. Experimental results show that this method surpasses existing approaches, achieving a recognition accuracy of 84.24% on the DeepShip dataset.

A proper filtering method for jump Markov system (JMS) is an effective approach for tracking a maneuvering target. Since the coexisting of heavy-tailed measurement noises (HTMNs) and one-step random measurement delay (OSRMD) in the complex scenarios of the surface maneuvering target tracking, the effectiveness of typical interacting multiple model (IMM) techniques may decline severely. To solve the state estimation problem in JMSs with HTMN and OSRMD simultaneously, this article designs a novel robust IMM filter utilizing the variational Bayesian (VB) inference framework. This algorithm models the HTMNs as student’s t-distribuitons, and presents a random Bernoulli variable to describe the OSRMD in JMSs. By transforming measurement likelihood function form from weighted summation to exponential product, this paper constructs hierarchical Gaussian state space models. Then, the state vectors, random Bernoulli vairable, and model probability are inferred jointly according to VB inference. The surface maneuvering target tracking simulation example result indicates that the presented IMM filter achieves superior target state estimation accuracy among existing IMM filters.