英文文献 科技类 原文及译文33

Multi-texture-model for Water Extraction Based on Remote Sensing Image

Hua WANG, Li PAN, Hong ZHENG

School of Remote Sensing and Information & Engineering, Wuhan University 129 Luoyu Road,

Wuhan 430079,P.R.China

School of Electronic Information, Wuhan University 129 Luoyu Road, Wuhan 430079,P.R.China

Abstract:

In this paper, a multi-texture-model for water extraction based on remote sensing imagery is proposed. The model is applied to extract inland water (including wide river, lake and reservoir)from high-resolution panchromatic images. Firstly directional variance is used to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.

1. Introduction

The recognition of water from remote sensing image has drawn considerable attention in recent yeas. A large number of publications about water extraction appeared and various approaches for water extraction have been proposed. Zhou developed a descriptive model for automatic extraction of water based on spectral characteristics[1]. Barton applied channel 4 for NOAA/AVHRR to extract water[2]. Du proposed a approach for water extraction from SPOT-5 based on decision tree algorithm[3]. Li recognized and monitored clear water from MODIS[4]. Wu extracted water from Quick Bird image and used active contour model to obtain accurate position of river bank[5]; In order to extract water from high-spatial remote sensing images, He used wavelet technique to expend the information and cleaned main noise of the images, and then presented multi-window linearity reserve technique to conserve linear water[6].

Recently, most research work on water extraction was forced on automatic recognition of water from remote sensing images based on spectral characteristics. However, there are some disadvantages of these methods: (1) The resolution of image used for water extraction is low. The minimum size of recognizable object is depended on the spatial resolution of sensor. Therefore it is difficult to obtain accurate position of water boundary. (2) Due to the characteristic of water itself and the sensor applied, in certain channels the spectral features of different objects are equilibrated. The equilibration leads to the phenomena of “different objects same image” or“different images same object”, which results in noise objects included in extraction result.

In this paper, a multi-texture-model for water extraction based on remote sensing is proposed. The model is applied to extract inland water (including wide river, lake and reservoir) from

high-resolution panchromatic image. Firstly directional variance is applied to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.

This paper is organized as follows. In Section 2, the directional variance model adopted is introduced. Then, fusion of proposed grain table model with directional variance model is discussed in Section 3.The experimental results of the proposed multi-texture-model and comparative studies with single models are given in Section 4. We conclude this paper in Section 5.