Loading [MathJax]/extensions/MathMenu.js
Haotian Zhang - IEEE Xplore Author Profile

Showing 1-14 of 14 results

Filter Results

Show

Results

Remote sensing multiview image segmentation is essential for achieving accurate and consistent stereoscopic perception of target scenes. This task involves processing RGB images from multiple viewpoints to generate high-accuracy, view-consistent semantic segmentation across all views. Traditional training-based methods struggle with maintaining cross-view consistency, while optimization-driven app...Show More
In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the Gaussian mixture models, can fit the distribution of latent variables more precisely, but the correspondin...Show More
Recently, the Mamba architecture based on state-space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied to remote sensing change detection (CD) tasks. However, most methods enhance the global receptive field by directly modifying the scanning mode of Mamba, neglecting the crucial role that local information plays in dense p...Show More
In the field of autonomous driving, a variety of sensor data types exist, each representing different modalities of the same scene. Therefore, it is feasible to utilize data from other sensors to facilitate image compression. However, few techniques have explored the potential benefits of utilizing inter-modality correlations to enhance the image compression performance. In this paper, motivated b...Show More
Semantic change detection (SCD) is an important task in geoscience and Earth observation. By producing a semantic change map for each temporal phase, both the land use land cover (LULC) categories and change information can be interpreted. Recently some multitask learning-based SCD methods have been proposed to decompose the task into semantic segmentation (SS) and binary change detection (BCD) su...Show More
Most approaches in learned image compression follow the transform coding scheme. The characteristics of latent variables transformed from images significantly influence the performance of codecs. In this paper, we present visual analyses on latent features of learned image compression and find that the latent variables are spread over a wide range, which may lead to complex entropy coding processe...Show More
Monitoring changes in the Earth’s surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing (RS) satellite imagery offers a unique perspective for monitoring these changes, leading to the emergence of RS image change interpretation (RSICI) as a significant research focus. Current RSICI technology e...Show More
Learned image compression methods have shown significant advances in performance. However, they often suffer from higher decoding complexity compared to traditional codecs. In this paper, we present an approach toward practical learned image compression that focuses on faster decoding by employing online encoder optimization. To reduce network complexity, we employ a hyperprior structure with smal...Show More
Remote sensing image change captioning (RSICC) aims to describe surface changes between multitemporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared). This poses challenges to spatial and temporal modeling of bi-temporal features. Despite previous methods progressing in the spatial change perceptio...Show More
Despite the success of deep learning-based change detection (CD) methods, their existing insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them insufficient capability in mitigating external factors (illumination changes and perspective differences) arising from different imaging conditions during CD. In this article, a bitemporal feature alignment (BiFA) model ...Show More
Leveraging the extensive training data from SA-1B, the segment anything model (SAM) demonstrates remarkable generalization and zero-shot capabilities. However, as a category-agnostic instance segmentation method, SAM heavily relies on prior manual guidance, including points, boxes, and coarse-grained masks. Furthermore, its performance in remote sensing image segmentation tasks remains largely une...Show More
Learned image compression (LIC) methods have made significant advances in recent years. In LIC, entropy model is an essential component, which utilizes conditional information to predict the probability distribution over the latent space. In the entropy models, many context models follow a spatially autoregressive paradigm, which leads to sequential coding. The autoregressive coding order, however...Show More
Most contemporary supervised remote sensing (RS) image change detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution CD, a.k.a., CD based on bitemporal images with different spatial resolutions. Given training samples of a fixed bitemporal resolution difference (ratio) between the high-resolution (HR) image and t...Show More
For current learned image compression methods, padding input images is necessary to meet the resolution requirements of down-sampling layers. However, the impact of padding has not been studied thoroughly. Most previous studies ignore padded images in the training process. In this paper, we analyze the impact of padding on compression performance. Then, we propose a padding-aware training (PAT) st...Show More