radres333 / VHRShips
This study focuses on all stages of ship classification in the optical satellite images. The proposed “Hierarchical Design (HieD)” approach, which is based on deep learning techniques, performs Detection, Localization, Recognition and Identification (DLRI) of the ships in the optical satellite images. HieD is an end-to-end approach which allows …
☆14Updated last year
Alternatives and similar repositories for VHRShips:
Users that are interested in VHRShips are comparing it to the libraries listed below
- [ICDM 2023] Code implementation of "Learning Efficient Unsupervised Satellite Image-based Building Damage Detection"☆27Updated last year
- ☆28Updated 2 years ago
- Codes for TGRS paper: DisOptNet: Distilling Semantic Knowledge from Optical Images for Weather-independent Building Segmentation☆16Updated 5 months ago
- How to Learn More? Exploring the Possibility of Kolmogorov-Arnold Networks for Hyperspectral Image Classification☆21Updated 5 months ago
- Image segmentation models for building localization and damage assessment based on satellite imagery from the xBD dataset which was used …☆42Updated last year
- Semantic-Aware Dense Representation Learning for Remote Sensing Image Change Detection☆35Updated 2 years ago
- ☆26Updated last year
- We present a novel complex-valued convolutional and multi-feature fusion network (CVCMFF Net) specifically for building semantic segmenta…☆23Updated 4 years ago
- Urban change detection with a Dual-Task Siamese network and semi-supervised learning☆22Updated 2 years ago
- ☆41Updated 2 years ago
- The physics guided and injected learning NN for SAR image patch-wise classification☆28Updated last year
- Code and experiments for the paper, "A Change Detection Reality Check", Corley et al.☆50Updated last week
- This repository contains code for experiments using the winning mode for xView2 "xView2: Assess Building Damage" challenge and a simple …☆25Updated last year
- A Cross-domain Change Detection Network Based on Instance Normalization☆12Updated last year
- Python codes for the paper "Deep SURE for unsupervised remote sensing image fusion", accepted in TGRS, 2022.☆15Updated last year
- TGRS2021: LGPNet for Building Change Detection☆33Updated 2 years ago
- Unified Change Detection Framework☆27Updated 5 months ago
- ☆14Updated 5 years ago
- A small codebase featuring a few elementary methods for SAR and Optical imagery data fusion