Context: Accurate citrus crop mapping is essential for agricultural monitoring, production planning, and supply-chain management, particularly in Brazil, one of the world’s leading orange producers and the leading orange-juice exporter. Satelli...
Crop maps are key inputs for crop inventory production and yield estimation and can inform the implementation of effective farm management practices. Producing these maps at detailed scales requires exhaustive field surveys that can be laborious, tim...
Accurate and large-scale crop mapping is crucial for global food security, yet its performance is often hindered by domain shift when models trained in one region are applied to another. This is particularly challenging in cross-continental scenarios...
The H.266/Versatile Video Coding (VVC) standard was developed to address the growing demand for compressing ultra-high-definition video content, supporting resolutions ranging from 4K to 8K and beyond. H.266/VVC improves coding efficiency by introduc...
This paper introduces a new memory-efficient algorithm for connected-components labeling in binary images, which is based on run-length encoding. Unlike conventional pixel-based methods that scan and label individual pixels using global buffers or di...
The increasing availability of medical images generated via different imaging techniques necessitates the need for their remote analysis and diagnosis, especially when such datasets involve brain morphological biomarkers, an important biological symm...
When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neur...
To achieve high performance, most deep convolutional neural networks (DCNNs) require a significant amount of training data with ground truth labels. However, creating ground-truth labels for semantic segmentation requires more time, human effort, and...
Multimodal hyperspectral and lidar data sets provide complementary spectral and structural data. Joint processing and exploitation to produce semantically labeled pixel maps through semantic segmentation has proven useful for a variety of decision ta...
To combine homomorphic public key encryption with reversible data hiding, a reversible data hiding scheme in homomorphic encrypted image based on EC-EG is proposed. Firstly, the cover image is segmented. The square grid pixel group randomly selected...
Skeletons are well-known descriptors used for analysis and processing of 2D binary images. Recently, dense skeletons have been proposed as an extension of classical skeletons as a dual encoding for 2D grayscale and color images. Yet, their encoding p...
Natural imagery segmentation has been transferred to land cover classification in remote sensing imagery with excellent performance. However, two key issues have been overlooked in the transfer process: (1) some objects were easily overwhelmed by the...
An important tool in the field of topological data analysis is persistent homology (PH), which is used to encode abstract representations of the homology of data at different resolutions in the form of persistence barcode (PB). Normally, one will obt...
The JPEG format, consisting of a set of image compression techniques, is one of the most commonly used image coding standards for both lossy and lossless image encoding. In this format, various techniques are used to improve image transmission and st...
In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semanti...
Hyperspectral Anomaly Detection (HAD) aims to detect the pixel or target whose spectral characteristics are significantly different from the surrounding pixels or targets. The effectiveness of reconstructing the background model is an essential eleme...
In recent years, vision–language models (VLMs) have been introduced into remote sensing semantic segmentation to provide richer semantic representations through visual–textual alignment. However, most existing VLM-based segmentation metho...
Pixel-level depth information is crucial to many applications, such as autonomous driving, robotics navigation, 3D scene reconstruction, and augmented reality. However, depth information, which is usually acquired by sensors such as LiDAR, is sparse....
Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate clas...
In 2018, Tseng et al. proposed a dual-image reversible embedding method based on the modified Least Significant Bit matching (LSB matching) method. This method improved on the dual-image LSB matching method proposed by Lu et al. In Lu et al.’s scheme...
Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB...
To address the difficulties in characterizing fine crack morphology, the limitations of detection accuracy, and the challenge of real-time deployment caused by large model parameter counts in concrete dam crack detection, this paper constructs DamCra...
Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in th...
Rapid and accurate landslide mapping is essential for disaster risk reduction and sustainable land management in landslide-prone mountainous regions. This study presents a U-Net semantic segmentation framework for pixel-wise landslide classification...
Steel structures are susceptible to corrosion due to their exposure to the environment. Currently used non-destructive techniques require inspector involvement. Inaccessibility of the defective part may lead to unnoticed corrosion, allowing the corro...
Hyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training o...
Image–text multimodal deep semantic segmentation leverages the fusion and alignment of image and text information and provides more prior knowledge for segmentation tasks. It is worth exploring image–text multimodal semantic segmentation...
Multi-branch networks combine backbones whose inductive biases are complementary, but their fusion modules set the branch weights from feature statistics alone and are therefore blind to what a pixel represents. We supervise fusion in label space ins...
Malware analytics suffer from scarce, delayed, and privacy-constrained labels, limiting fully supervised detection and hampering responsiveness to zero-day threats. We propose SMAD, a Semi-supervised Android Malicious App Detector that integrates a s...
Dental caries remains a prevalent diagnostic challenge, particularly in periapical radiographs, where geometric distortions and anatomical overlap complicate interpretation. Although deep learning has advanced dental image analysis, most segmentation...
Recent developments in smartphone-based strip readers have further improved the performances of lateral flow test kits. Most smartphone cameras encode an unaltered and nonlinear power-law transfer function that maps the light intensity to a pixel val...
In order to solve various security risks faced by image privacy protection, we propose a 3D cuboid image encryption scheme based on message-encoded controlled alternate quantum walks. Firstly, we calculated the initial parameters of the quantum syste...
Background: Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system, affecting more than 2.8 million individuals worldwide. Automated segmentation of white matter lesions on fluid-attenuated inversion recov...
In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcra...
Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used...
A simple-to-implement and easy-to-integrate strategy for image processing is proposed in this paper, which effectively and efficiently optimizes the power consumption of both DRAM and SRAM. Since the power consumption of DRAM is proportional to the n...
In order to solve the problem of manual labeling in semi-supervised tree species classification, this paper proposes a pixel-level self-supervised learning model named M-SSL (multisource self-supervised learning), which takes the advantage of the inf...
Remote sensing technologies are suitable for detecting marine oil-gas leakages on a large scale. It is important to structure an accurate method for detecting marine oil-gas leakages in varied remote sensing images. However, traditional spectral inde...
Medical image segmentation requires dense pixel-level annotations, making large-scale dataset construction expensive and motivating research into data-efficient training. The main objective of this paper is to determine whether fusing two complementa...
Autonomous navigation in unknown environments demands policies that can jointly perceive semantic context and geometric safety. Existing Transformer-enabled deep reinforcement learning (DRL) frameworks, such as the Goal-guided Transformer Soft Actor&...
Spoofing attacks (or Presentation Attacks) are easily accessible to facial recognition systems, making the online financial system vulnerable. Thus, it is urgent to develop an anti-spoofing solution with superior generalization ability due to the hig...
In recent years, a novel matching classification strategy inspired by the artificial deoxyribonucleic acid (DNA) technology has been proposed for hyperspectral remote sensing imagery. Such a method can describe brightness and shape information of a s...
Event cameras are bio-inspired devices that have revolutionized the acquisition of visual information by mimicking the neural architecture of the eye. These cameras respond asynchronously to changes in scene illumination at the pixel level, providing...
Microwave imaging is promising for non-contact inspection of radar-absorbing materials (RAMs); however, strong structured non-damage responses in reconstructed microwave images can resemble surface defects and trigger false detections, while hard mas...
We develop a novel compressive coded rotating mirror (CCRM) camera to capture events at high frame rates in passive mode with a compact instrument design at a fraction of the cost compared to other high-speed imaging cameras. Operation of the CCRM ca...
Designing and optimizing systems by end-to-end deep learning is a recently emerging field. We present a novel physics-constrained autoencoder (PyCAE) for the design and optimization of a physically realizable sensing model. As a case study, we design...
A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteri...
Promptable medical segmentation models can be sensitive to small targets, weak boundaries, heterogeneous texture, and inaccurate user prompts. We propose HBF-BCER, a prompt-preserving adaptation of the SAM ViT-B architecture for medical image segment...
Hyperspectral image classification relies heavily on the effective modeling of spatial–spectral representations. Recent deep learning architectures, including Transformers and state space models (SSMs), have shown promise for HSI classification...
Semi-supervised joint classification of Hyperspectral Images (HSIs) and LiDAR-derived Digital Surface Models (DSMs) remains challenging due to scarcity of labeled pixels, strong intra-class variability, and the heterogeneous nature of spectral and el...
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