Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale

Abstract

Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and/or shaded scene. The presented approach focuses on object-based change detection with joint use of spatial and spectral information, referring to it as multi-level spatial analyses. The analyses are conducted in three phases: (1) The pixel-level spatial analysis is performed by adding the density dimension into a multi-feature space for classification to indicate the spatial dependency between pixels; (2) The member-level spatial analysis is conducted by the self-adaptive morphology to readjust the incorrectly classified members according to the spatial dependency between members; (3) The object-level spatial analysis is reached by the self-adaptive morphology involved with the additional rule of sharing boundaries. Spatial analysis at this level will help detect spurious change objects according to the spatial dependency between objects. It is revealed that the error from the automatically extracted vegetation objects with the pixel-and member-level spatial analyses is no more than 2.56%, compared with 12.15% without spatial analysis. Moreover, the error from the automatically detected spurious changes with the object-level spatial analysis is no higher than 3.26% out of all the dynamic vegetation objects, meaning that the fully automatic detection of vegetation change at a joint maximum error of 5.82% can be guaranteed.

Vegetation Dynamics and Associated Driving Forces in Eastern China during 1999–2008

“…Over the past 10 years, there was large-scale urbanization processes in Eastern China [55,56], which made it possible to study the impact of anthropogenic factors on vegetation activities on a large scale in this region. Variations of the vegetation activities between urban and rural areas have received much attention from researchers [57][58][59]. However, many studies have focused on the spatial impact of urbanization on vegetation while ignoring the temporal impact of urbanization on vegetation.…”

Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral–Spatial Features

“…CD techniques are often applied in natural disaster evaluation, urban sprawl assessment, and environmental monitoring [2]- [5].…”

“…As a result, making full use of the spatial information has become an exploitation goal for these data. The most commonly used methods for extracting information from VHR images is objectbased image analysis [2], [25], [26]. However, this approach heavily depends on the accuracy of the initial segmentation [9], [19], [27].…”

A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space

“…After extracting the precise training data, the mean of precise changed training samples was calculated in order to achieve the reference change vector. Then, using the Equations (13)- (16) and the reference vector from the previous step, all pixels of the image are transformed into the similarity space. In this paper, the multi-spectral data are transformed to similarity spaces using Spectral Distance-Angle-Correlation-Spectral Value (SDACV) features [34].…”

“…In direct classification method, multi-temporal images are stacked together and then classified directly in order to detect the land cover transition [16]. In both classification based methods, labeled samples are essential for training the supervised classifiers.…”