Change Detection in Stockholm between 1986 and 2006 using SPOT Multispectral and Panchromatic Data

Sammanfattning: With an increasing urban population in Sweden, expecting to reach 90% by 2050 (UN World Urbanization prospects, The 2011 Revision), this high level of urban population put pressure on functioning infrastructure, sufficient housing and need to monitor the environmental effects such as pollution and the effects of land use change. Stockholm County currently holds 22% of the population and accounts for nearly half of the urban growth in Sweden (Svensk Handelskammare).   Previous research on change detection using remote sensing cover the use of data sets from optical sensors, infrared spectrum, radar data and the use of additional derived data sets such as indices and texture measure (implemented on pixel or feature level). There is not yet any consensus regarding which change detection methods that is superior to others. Comparative studies often only test a few algorithms on one particular data set. Change detection of Stockholm urban area has not been well investigated in previous literature. This thesis is focused on a change detection analysis of Stockholm area between 1986 and 2006 using remote sensing data fusion. The data set used is SPOT-1 HRV XS data at 20m resolution from 1986, SPOT-1 HRV Panchromatic data at 10m resolution from 1987 and SPOT-5 HRG XS data of 10m resolution from 2006. The first challenge was to fuse the multispectral and panchromatic images from 1986 and 1987 to inject the details of the 10m panchromatic image into the 20m multispectral so that the resulting images will have similar spatial details as the 2006 images. This was done by wavelet transform. Haar, Daubechies, Coiflet and Biorthogonal wavelet families were tested to find the optimal fusion and the corresponding parameters. The results showed that the Daubechies, Coiflet and Biorthogonal families did not differ significantly and that for this data set and analysis purpose more than one wavelet family fusion results showed satisfactory results. The correlation coefficient for these three families was all over 0,96 at decomposition level two.   Then change detection was performed using change vector analysis (CVA) and a supervised non-parametric classifier. A comparison is made between two inputs: one using only spectral information and the other adding textural information to the spectral information. The change detection analysis was undertaken in three steps: calculating texture measures from the original images, calculating change magnitude using Change Vector Analysis (CVA) and classifying change from no-change using Support Vector Machine (SVM). Three GLCM texture measures were chosen: Homogeneity, Mean and Entropy in the change detection analysis. These, as well as the spectral information, were input for change vector magnitude. Then SVM is used to classify changed pixels from no-change pixels. Two change results were obtained, the first using only spectral information, and the other using both spectral and textural information. The overall accuracy using only spectral information was rather high at 87, 86%. But the visual inspections indicate that using only spectral change magnitude is not sufficient for a good change detection result because there is an apparent overestimation of change. When adding the textural information the overall accuracy increase drastically to 97,01%, although at visual inspection there seem to be an underestimation of change.  Because of the high overall accuracy an independent validation was made causing the overall accuracy and kappa to decrease. Change detection using only multispectral data got an overall accuracy of 76, 12% and kappa coefficient 0,53. For change detection result with added texture measures the overall accuracy became 85,80%  and 0,72.  The results further confirm the general advantages using texture measure although the independent evaluation resulted in a lower accuracy than the author's evaluations.