Remote Sensing Time Series Image Processing

Qihao Weng

• Publisher: CRC Press
• Year: 2018
• Topic: Technology & Engineering
• eBook ISBN:
  9781351680561
• Subtopic: Digital Media
• Index: Technology & Engineering

Today, remote sensing technology is an essential tool for understanding the Earth and managing human-Earth interactions. There is a rapidly growing need for remote sensing and Earth observation technology that enables monitoring of world's natural resources and environments, managing exposure to natural and man-made risks and more frequently occurring disasters, and helping the sustainability and productivity of natural and human ecosystems. The improvement in temporal resolution/revisit allows for the large accumulation of images for a specific location, creating a possibility for time series image analysis and eventual real-time assessments of scene dynamics. As an authoritative text, Remote Sensing Time Series Image Processing brings together active and recognized authors in the field of time series image analysis and presents to the readers the current state of knowledge and its future directions.

Divided into three parts, the first addresses methods and techniques for generating time series image datasets. In particular, it provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications. Part II examines feature development and information extraction methods for time series imagery. It presents some key remote sensing-based metrics, and their major applications in ecosystems and climate change studies. Part III illustrates various applications of time series image processing in land cover change, disturbance attribution, vegetation dynamics, and urbanization.

This book is intended for researchers, practitioners, and students in both remote sensing and imaging science. It can be used as a textbook by undergraduate and graduate students majoring in remote sensing, imaging science, civil and electrical engineering, geography, geosciences, planning, environmental science, land use, energy, and GIS, and as a reference book by practitioners and professionals in the government, commercial, and industrial sectors.

Part I

Time Series Image/Data Generation

1

Cloud and Cloud Shadow Detection for Landsat Images: The Fundamental Basis for Analyzing Landsat Time Series

Zhe Zhu, Shi Qiu, Binbin He, and Chengbin Deng

Contents

Brief Summary

1.1Introduction

1.2Landsat Data and Reference Masks

1.2.1Landsat Data

1.2.2Manual Masks of Landsat Cloud and Cloud Shadow

1.3Cloud and Cloud Shadow Detection Based on a Single-Date Landsat Image

1.3.1Physical-Rules-Based Cloud and Cloud Shadow Detection

1.3.1.1Physical-Rules-Based Cloud Detection Algorithms

1.3.1.2Physical-Rules-Based Cloud Shadow Detection Algorithms

1.3.2Machine-Learning-Based Cloud and Cloud Shadow Detection

1.4Cloud and Cloud Shadow Detection Based on Multitemporal Landsat Images

1.4.1Cloud Detection Based on Multitemporal Landsat Images

1.4.2Cloud Shadow Detection Based on Multitemporal Landsat Images

1.5Discussions

1.5.1Comparison of Different Algorithms

1.5.2Challenges

1.5.3Future Development

1.5.3.1Spatial Information

1.5.3.2Temporal Frequency

1.5.3.3Haze/Thin Cloud Removal

1.6Conclusion

References

Brief Summary

Cloud and cloud shadow detection is the fundamental basis for analyzing Landsat time series. This chapter provides a comprehensive review of all the cloud and cloud shadow detection algorithms designed explicitly for Landsat images. This review provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications using Landsat time series.

1.1Introduction

Landsat satellites have been widely used for a variety of remote sensing applications, such as change detection (Collins and Woodcock, 1996; Xian et al., 2009), land cover classification (Homer et al., 2004; Yuan et al., 2005), biomass estimation (Zheng et al., 2004; Lu, 2005), and leaf area index retrieval (Chen and Cihlar, 1996; Fassnacht et al., 1997). Nevertheless, for decades, most of the analyses were based on a single or a few cloud free Landsat images acquired at different dates, due to the high cost of Landsat images prior to 2008 (Loveland and Dwyer, 2012). Free and open access to the entire Landsat archive in 2008 has changed the story entirely (Woodcock et al., 2008; Wulder et al., 2012). Landsat data are being downloaded for an unprecedented variety of applications. Many of them require frequent Landsat observations for the same location – Landsat Time Series (LTS). The Landsat Global Archive Consolidation (LGAC) initiative has added 3.2 million Landsat images to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center (Wulder et al., 2016), which has made time series analysis with LTS even more popular. Decreasing data storage costs and increasing computing power have further stimulated the use of LTS.

Though time series analysis based on LTS has attracted much attention, automated cloud and cloud shadow detection has been and remains a major obstacle. The presence of clouds and cloud shadows reduces the usability of the Landsat image which makes it difficult for any kind of remote sensing applications. For coarse resolution images, such as from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), there are many mature operational algorithms for detecting clouds and cloud shadows (Derrien et al., 1993; Ackerman et al., 1998). However, for moderate resolution satellites, like Landsat, there were no algorithms that could provide cloud and cloud shadow masks at the pixel level. This is not surprising because Landsat images were not affordable, each of which previously cost more than 400 U.S. dollars per image. Even when cloudy Landsat images are used, most of the time only a small number of images are needed, and manual interpretation of clouds and their shadows in the images is feasible. However, when these financial constraints were lifted (Woodcock et al., 2008), an unprecedented demand arose for automatically processing a massive number of Landsat images for time series analysis. Manual interpretation of cloud and cloud shadow was no longer acceptable.

1.2Landsat Data and Reference Masks

1.2.1Landsat Data

Since 1972, Landsat satellites have provided a continuous Earth observation data record. Landsats 1–5 carried the Multispectral Scanner System (MSS) sensor with 60-meter spatial resolution. The MSS only collected images with four spectral bands, including green, red, and two Near InfraRed (NIR) bands (Table 1.1). Note that the Landsat 3 MSS also included a Thermal Infrared (TIR) band, but failed ...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Preface
• Acknowledgments
• Editor
• Contributors
• Part I: Time Series Image/Data Generation
• Part II: Feature Development and Information Extraction
• Part III: Time Series Image Applications
• Index