Evaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration

Jindong Wu,Marvin Bauer

doi:10.3390/rs5094450

Abstract

Shadows in high resolution imagery create significant problems for urban land cover classification and environmental application. We first investigated whether shadows were intrinsically different and hypothetically possible to separate from each other with ground spectral measurements. Both pixel-based and object-oriented methods were used to evaluate the effects of shadow detection on QuickBird image classification and spectroradiometric restoration. In each method, shadows were detected and separated either with or without histogram thresholding, and subsequently corrected with a k-nearest neighbor algorithm and a linear correlation correction. The results showed that shadows had distinct spectroradiometric characteristics, thus, could be detected with an optimal brightness threshold and further differentiated with a scene-based near infrared ratio. The pixel-based methods generally recognized more shadow areas and with statistically higher accuracy than the object-oriented methods. The effects of the prior shadow thresholding were not statistically significant. The accuracy of the final land cover classification, after accounting for the shadow detection and separation, was significantly higher for the pixel-based methods than for the object-oriented methods, although both achieved similar accuracy for the non-shadow classes. Both radiometric restoration algorithms significantly reduced shadow areas in the original satellite images.

Highlights

Detailed and accurate land use and land cover information is essential to document the current state of urban environment, to evaluate what and where changes have been made on the landscape, and to examine the possible impacts on ecological processes and climate
We examined the histogram of object brightness based on the image segmentation, did not find an obvious threshold value between shadow and non-shadow objects
The analysis indicated that different types of shadow (i.e., shadows on grass (SOG) and SOI) had distinct spectroradiometric characteristics even though both had an overall low brightness (Table 1)

Summary

Introduction

Detailed and accurate land use and land cover information is essential to document the current state of urban environment, to evaluate what and where changes have been made on the landscape, and to examine the possible impacts on ecological processes and climate. It has been widely recognized for decades that satellite observations of the Earth’s surface can be used to map land use and land cover. Cast by elevated ground objects such as trees and buildings, regularly exist in high resolution images largely due to the narrow field of view of satellite sensors as well as the low solar elevation at the time of image acquisition. A significant proportion of high spatial resolution imagery in urban areas can be affected by shadows; this creates great difficulty in directly applying imagery data to analyze urban land use and land cover [2]

Methods

Results

Discussion

Conclusion