SHIP DETECTION BASED ON MULTIPLE FEATURES IN RANDOM FOREST MODEL FOR HYPERSPECTRAL IMAGES

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Ding, L.^1¹  Li, N.^1² 

[1] China Geological Survey, Beijing, China^2;School of Instrumentation Science and Opto-electronics Engineering, Beihang University, 37 Xueyuan Road, Beijing, China^1

关键词: Hyperspectral Image;    Ship Detection;    Multiple Feature;    Spectral Feature;    Texture Feature;    Random Forest (RF);   

DOI  :  10.5194/isprs-archives-XLII-3-891-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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.

【 授权许可】

CC BY