A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas

Recognizing the semantic categories of low-resolution (LR) aerial photos is an indispensable technique in geoscience and remote sensing. However, it is also a challenging task in practice. In this work, a semi-supervised perceptual feature selection (SPFS) pipeline is proposed for LR aerial photo categorization, focusing on selecting high quality perception-guided visual features. Specifically, by mimicking human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually or semantically salient foreground regions coupled with the background non-salient regions. This model can 1) produce the a gaze shifting path (GSP) simulating human gaze behavior, and 2) generate hierarchical deep representation for a GSP. Afterward, a semi-supervised feature selection (FS) is leveraged toward a succinct set of discriminative deep GSP features, wherein only labels of LR aerial photos are required. Based on the selected features, a classifier is trained for visual categorization. Comprehensive experimental results have validated our method's advantage.

Section: Discussionmentioning

confidence: 99%

LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Li,

Sheng

2023

“…Recognizing aerial images is an indispensable application in remote sensing [21][22][23][24][25]. We proposed a novel crossresolution-enhanced high-resolution aerial photo categorization pipeline, wherein deep perceptual features are extracted and refined by propagating the prior knowledge of low-resolution aerial photos into high-resolution ones.…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Aerial Photo Categorization Model by Cross-Resolution Perceptual Experiences Transfer

Li,

Liu

2023

There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with high-resolution and usually contain tens to hundreds of ground objects (e.g., vehicles and rooftops). Understanding the categories of a rich variety of high-resolution aerial photos is an indispensable technique for many applications, such as intelligent transportation, natural disaster prediction, and smart agriculture. In this work, we propose a cross-resolution perceptual experiences transfer framework for categorizing highresolution aerial photos, focusing on leveraging the perceptual features from low-resolution aerial photos to enhance the feature selection of high-resolution ones. More specifically, we first construct gaze shifting path to mimic human visual perception to both low-resolution and high-resolution aerial photos, wherein the corresponding deep gaze shifting path features are engineered. Afterward, a kernel-induced feature selection algorithm is formulated to obtain a succinct set of deep gaze shifting path features discriminative across lowand high-resolution aerial photos. Based on the selected features, low-and high-resolution aerial photos' labels are collaboratively utilized to train a linear classifier for categorizing high-resolution ones. Extensive comparative studies have validated the superiority of our method.

“…Identifying the category labels of LR aerial image is an important task in intelligent systems [20], [21], [22], [23], [24]. This work introduces a new LR aerial image recognition framework.…”

Section: Summary and Future Workmentioning

confidence: 99%

Low-Resolution Aerial Imagery Classification by Actively Learning Human Perception and Manifold-Regularized Feature Selection

Xu,

Liu,

Wang

2024

There are plenty of high-and low-altitude earth observation satellites asynchronously capture massive-scale aerial images everyday. In practice, high-altitude satellites take low-resolution (LR) aerial pictures, each covers a considerably large area. Comparatively, low-altitude satellites capture high-resolution (HR) aerial photos, each depicts a relatively small area. Effectively identifying the LR aerial images' semantic categories is an indispensable module in many AI systems. However, it is also a challenging task due to: 1) the inefficiency to label adequate training samples, and 2) the difficulty to describe how humans preserving the world. To handle these problems, this work presents a so-called active perception learning coupled with a manifold-regularized feature selection (MRFS), aiming at acquiring perceptual and discriminative visual representation to classify LR aerial photos. Particularly, by stimulating how humans sequentially perceiving different salient regions, we deploy an active learning paradigm to divide an LR aerial image into a few attractive regions as well as a rich set of non-attractive regions. Theoretically, the active learning technique ensures that the selected attractive regions can maximally reconstruct each LR aerial image, which well mimics human visual perception. Subsequently, a novel MRFS is designed to select high quality features from the actively detected attractive regions. MRFS has many advantages: 1) it is built upon a semi-supervised architecture that a small proportion of labeled samples are required; 2) a linear classifier based on the selected features can be learned in the unified MRFS framework; 3) the labeled/unlabeled sample distribution are optimally preserved during feature selection (FS). Plenty of empirical results shown the superiority of the proposed classification pipeline.