Remote Sensing Image Segmentation and Representation through Multiscale Analysis

J. A. D. Santos, R. Torres
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引用次数: 2

Abstract

Every year, new sensor technologies are being implemented to improve the acquisition of high-resolution remote sensing images (RSIs). With the large amount of data provided by these sensors, novel computational approaches are constantly required to support the decision-making process based on RSI analysis. A typical problem is the recognition of target regions for land-cover mapping. In this context, the main problems are: (1) classification methods are dependent on the segmentation quality; and (2) the selection of representative samples for training is a costly process. The samples indicated by the user are not always enough to define the best segmentation scale. Furthermore, the indication of samples can be expensive, since it often requires to visit studied places in loco. The segmentation-dependence problem has been addressed in the literature by using multiscale analysis. The training sample selection problem is, in turn, addressed mainly by employing user interaction techniques which are usually combined with pixel-based classification approaches. This work aims to introduce problems, challenges, and some state-of-the-art approaches for multiscale classification of remote sensing image. The main covered topics are arranged into four sessions: research challenges, segmentation, feature extraction, and classification.
基于多尺度分析的遥感图像分割与表示
每年都在实施新的传感器技术,以改善高分辨率遥感图像(rsi)的获取。由于这些传感器提供了大量数据,因此不断需要新的计算方法来支持基于RSI分析的决策过程。一个典型的问题是土地覆盖制图中目标区域的识别。在此背景下,主要问题有:(1)分类方法依赖于分割质量;(2)选择有代表性的样本进行训练是一个代价高昂的过程。用户指示的样本并不总是足以定义最佳分割尺度。此外,样品的指示可能是昂贵的,因为它经常需要实地访问研究地点。分割依赖问题已经在文献中通过多尺度分析得到解决。训练样本选择问题,反过来,主要是通过使用用户交互技术,通常结合基于像素的分类方法来解决。本文旨在介绍遥感图像多尺度分类中存在的问题、面临的挑战和一些最新的方法。主要涵盖的主题分为四个部分:研究挑战,分割,特征提取和分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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