6 citations · 11 across the 12 of their papers we have counts for
13 papers
Reflectance Multispectral Imaging for Soil Composition Estimation and USDA Texture Classification
G. A. S. L Ranasinghe, J. A. S. T. Jayakody, M. C. L. De Silva +5
Soil texture is a foundational attribute that governs water availability and erosion in agriculture, as well as load bearing capacity, deformation response, and shrink-swell risk i…
Computer Vision based group activity detection and action spotting
Narthana Sivalingam, Santhirarajah Sivasthigan, Thamayanthi Mahendranathan +3
Group activity detection in multi-person scenes is challenging due to complex human interactions, occlusions, and variations in appearance over time. This work presents a computer…
Enhanced SCanNet with CBAM and Dice Loss for Semantic Change Detection
Athulya Ratnayake, Buddhi Wijenayake, Praveen Sumanasekara +3
Semantic Change Detection (SCD) in remote sensing imagery requires accurately identifying land-cover changes across multi-temporal image pairs. Despite substantial advancements, in…
A Novel Algorithm for Digital Lithological Mapping-Case Studies in Sri Lanka's Mineral Exploration
R. M. L. S. Ramanayake, D. C. Dammage, I. Z. M. Zumri +8
Conventional manual lithological mapping (MLM) through field surveys are resource-extensive and time-consuming. Digital lithological mapping (DLM), harnessing remotely sensed spect…
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing
R. M. K. L. Ratnayake, D. M. U. P. Sumanasekara, H. M. K. D. Wickramathilaka +3
In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. The attention mechan…
Dual mode multispectral imaging system for food and agricultural product quality estimation
Darsha Udayanga, Ashan Serasinghe, Supun Dassanayake +4
Multispectral imaging coupled with Artificial Intelligence, Machine Learning and Signal Processing techniques work as a feasible alternative for laboratory testing, especially in f…
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction
Mevan Ekanayake, Zhifeng Chen, Mehrtash Harandi +2
In Magnetic Resonance Imaging (MRI), image acquisitions are often undersampled in the measurement domain to accelerate the scanning process, at the expense of image quality. Howeve…
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification
Mevan Ekanayake, Kamlesh Pawar, Gary Egan +1
Deep learning (DL) models are capable of successfully exploiting latent representations in MR data and have become state-of-the-art for accelerated MRI reconstruction. However, und…
Object Dimension Extraction for Environment Mapping with Low Cost Cameras Fused with Laser Ranging
E. M. S. P. Ekanayake, T. H. M. N. C. Thelasingha, U. V. B. L. Udugama +4
It is essential to have a method to map an unknown terrain for various applications. For places where human access is not possible, a method should be proposed to identify the envi…
Laser Ranging Based Intelligent System for Unknown Environment Mapping
T. H. M. N. C. Thelasingha, U. V. B. L. Udugama, E. M. S. P. Ekanayake +4
This work describes the implementation of a simple and computationally efficient Intelligent Navigation System (INS) for autonomous systems used in areas where human access is impo…
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction
Mevan Ekanayake, Kamlesh Pawar, Mehrtash Harandi +2
Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI)…
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness
Yasiru Ranasinghe, Kavinga Weerasooriya, Roshan Godaliyadda +6
In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a s…
Convolutional Autoencoder for Blind Hyperspectral Image Unmixing
Yasiru Ranasinghe, Sanjaya Herath, Kavinga Weerasooriya +4
In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel into two fundamental representatives: endmembers and abundances. In this paper, a novel ar…