Ship classification from overhead imagery using synthetic data and domain adaptation
In this paper, we revisit the problem of classifying ships (maritime vessels) detected from overhead imagery. Despite the last decade of research on this very important and pertinent problem, it remains largely unsolved. One of the major issues with the detection and classification of ships and other objects in the maritime domain is the lack of substantial ground truth data needed to train state-of-the-art machine learning algorithms. We address this issue by building a large (200k) synthetic image dataset using the Unity gaming engine and 3D ship models. We demonstrate that with the use of synthetic data, classification performance increases dramatically, particularly when there are very few annotated images used in training.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationGeneral ClassificationUnitySimilar Papers 제목 키워드 기반
Comprehensive Dataset of Synthetic and Manipulated Overhead Imagery for Development and Evaluation of Forensic Tools
We present a first of its kind dataset of overhead imagery for development and evaluation of forensic tools. Our dataset consists of real, fully synthetic and partially manipulated overhead imagery generated from a custo…
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e.g., satellite) imagery. One ongoing challenge however is the acquisition of training data, due to high costs of ob…
Few-Shot Learningobject-detectionObject DetectionThe Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite) imagery benchmarks. However, these benc…
Synthetic Data and Hierarchical Object Detection in Overhead Imagery
The performance of neural network models is often limited by the availability of big data sets. To treat this problem, we survey and develop novel synthetic data generation and augmentation techniques for enhancing low/z…
object-detectionObject DetectionStyle TransferSynthetic Data GenerationUsing Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image. We propose a novel conditional generative adversarial network (cGAN) in which the trained generator g…
DecoderGeneral ClassificationGenerative Adversarial NetworkLand Cover Classification