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- Title
SHIP DETECTION BASED ON MULTIPLE FEATURES IN RANDOM FOREST MODEL FOR HYPERSPECTRAL IMAGES.
- Authors
Na Li; Ling Ding; Huijie Zhao; Jia Shi; Daming Wang; Xuemei Gong
- Abstract
A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.
- Subjects
RECOGNITION of ships; RANDOM forest algorithms; HYPERSPECTRAL imaging systems
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2018, Vol 42, Issue 3, p891
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLII-3-891-2018