Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution

Autor: Wenzhong Shi, Zhiyong Lv, Ning Xiaojuan, Jon Atli Benediktsson
Přispěvatelé: Rafmagns- og tölvuverkfræðideild (HÍ), Faculty of Electrical and Computer Engineering (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskóli Íslands, University of Iceland
Rok vydání: 2016
Předmět:
010504 meteorology & atmospheric sciences
Computer science
Remote sensing application
Science
very high spatial resolution (VHSR) aerial image
multi-scale segmentation
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
0211 other engineering and technologies
02 engineering and technology
Land cover
Myndvinnsla
01 natural sciences
Composite image filter
Litrófsgreining
land cover classification
Landmælingar
Image noise
Image filter
Loftmyndir
Segmentation
Computer vision
Aerial image
Image resolution
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Spatial resolution
business.industry
image filter
Filter (signal processing)
Object (computer science)
Multiscale segmentation
Feature (computer vision)
General Earth and Planetary Sciences
Recursive filter
Artificial intelligence
Land cover classification
business
Zdroj: Remote Sensing; Volume 8; Issue 12; Pages: 1023
Remote Sensing, Vol 8, Iss 12, p 1023 (2016)
ISSN: 2072-4292
DOI: 10.3390/rs8121023
Popis: Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%–18% improvement in overall accuracy.
This work was supported by the Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (2015NGCM) and the project from the China Postdoctoral Science Foundation (2015M572658XB), and the National Natural Science Foundation of China (61302135).
Databáze: OpenAIRE
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