Visual selective attention, driven by individual preferences, regulates human prioritization of visual stimuli by bridging subjective cognitive mechanisms with objective visual elements, thereby steering the semantic interpretation and hierarchical processing of dynamic visual scenes. However, existing models and datasets predominantly neglect the influence of subjective cognitive diversity on fixation behavior. Conventional saliency prediction models, typically employing segmentation approaches, rely on low-resolution imagery to generate saliency heatmaps, subsequently upscaled to native resolutions, which limiting their capacity to capture personalized attention patterns. Furthermore, MLLMs are constrained by factors such as hallucinations, making it very costly to strictly adhere to the expected format in tasks involving multiple point predictions, and achieving precise point positioning is challenging. To address these limitations, we present Subjective Personalized Attention for Ad vertisement Videos, namely SPA-ADV, a large-scale multimodal dataset capturing gaze behaviors from over 4,500 participants varying in age and gender with 486 videos. Furthermore, we propose PRE-MAP, a novel eye-tracking saliency model that characterizes Personalized visual disparities through Reinforcement learning-optimized Eye-tracking, built upon MLLMs and guided by Multi-Attribute user profiles to predict Points. To ensure MLLMs produce prediction points that are both format-correct and spatially accurate, we introduce Consistency Group Relative Policy Optimization (C-GRPO), inspired by the variability in eye movement points and Multi-Attribute profiles. Extensive experiments on SPA-ADV and other benchmarks demonstrate the effectiveness of our approach. The code and dataset are available at https://github.com/mininglamp-MLLM/PRE-MAP.

Hyperspectral remote sensing image feature selection enhances the efficiency of further applications by extracting band information. However, it is challenging to optimize and extract the spectral relationship information of the entire hyperspectral image using traditional methods and solidified spectral representation strategies. As a result, band selection often leads to locally optimal candidate solutions. For instance, when applied to downstream classification tasks, the selected bands typically exhibit issues such as poor information separability, high spectral correlation, and missing information. This paper proposes a new HSIAO_BS framework based on Jeffries-Matusita distance (JM) and an evolutionary algorithm to obtain an excellent subset of bands for hyperspectral remote sensing image feature selection addressing downstream classification tasks. The research problem is modeled as a solution space with effective inter-spectral relationship representation. The HSIAO_BS framework designs an adaptive band encoding mechanism and a feature relationship representation based on JM distance to construct this space. Additionally, the key optimized search method in HSIAO_BS is the improved HSIAO. This evolutionary algorithm combines differential crossover and attenuating mutation strategies to enhance the balance between global exploration and local exploitation capabilities, while also targeting to improve the preference for band selection. The reliability, validity, and stability of the HSIAO_BS framework are verified through a series of performance test experiments conducted on three hyperspectral remote sensing image datasets to support downstream classification tasks.

Optical remote sensing image cloud removal is a crucial area of research within remote sensing technology. Its primary goal is to recover surface information from images obscured by clouds. Although existing generative models have achieved significant success in cloud-free image reconstruction, providing important support for downstream tasks, challenges remain in capturing and reconstructing complex detailed features. To address these issues, this article presents a dual-branch diffusion residual reconstruction framework for optical remote sensing image cloud removal, called the DBD-CR framework. Specifically, in the initial reconstruction stage, we utilize a diffusion model to obtain the initial reconstructed image from the cloudy input. Next, in the discrepancy correction stage, we calculate the residual texture map between the cloudy image and the initial reconstructed image to capture the details of the surface features that have not been restored. In addition, we integrate a frequency domain enhancement interaction attention module and an adaptive mixed attention module (AMAM) into the denoising diffusion model. These components capture crucial details in the frequency domain and adaptively select features that combine downsampling and upsampling. By adopting more comprehensive evaluation metrics and conducting extensive experiments on three public remote sensing cloud removal datasets, the DBD-CR framework achieves a maximum reduction of 18.081 in the Fréchet inception distance (FID) metric and a maximum improvement of 0.902 in the peak signal-to-noise ratio (PSNR) metric, further demonstrating its advanced performance for optical remote sensing image cloud removal tasks.

A prerequisite for solving many agricultural problems is to accurately estimate the area affected by crop disasters and its severity rating. In this letter, we propose a pipeline to segment the drought area and distinguish the severity rating of the maize on RGB images accessed by an unmanned aerial vehicle (UAV) through a semantic segmentation method based on deep learning. First, the ground truth is created through expert evaluation and visual interpretation with the aid of the Normalized Difference Vegetation Index (NDVI). The neural network structure that was used is based on U-Net. Some structural and parameter improvements on U-net were made using SE-ResNeXt-50 as the backbone with the atrous spatial pyramid pooling (ASPP) module. By using RGB images as the input of the neural network for training, the final trained network can work on RGB images captured by a consumer UAV. The experimental results showed that our pipeline achieved an F1-score of 0.9034 and a Jaccard index of 0.8287 on the test set.

Recent years LiDAR (Light Detection and Ranging) data is widely used for constructing 3D terrain models which provide realistic impressions of the urban environment. This paper presents a hierarchical contour method for building boundary extraction and 3D reconstruction from LiDAR Data. This method provides acceptable visualization for large-scale poor quality LiDAR data reconstruction.

Recent years LiDAR data is widely used for constructing 3D terrain models which provide realistic impressions of the urban environment. This paper presents an automatic method for extracting 3D building model by the fusion of LiDAR data, 2D building outlines and orthoimage. 2D building outlines is generated by classifying the LiDAR data to terrain and off-terrain points, then detecting building edges points through step-structure detector and generalization. 2D building boundaries are added on the DSM (Digital Surface Model) from LiDAR data to generate complex buildings by using CSG with the Boolean operations of union, intersection and differences.