Change Detection Unclassified images can be compared on a pixel-by-pixel or patch-by-patch basis; Classified images can be compared with the results indicating changes in specific classes over time. Change Detection • Georeferencing of each image must be relatively precise. Spatial offsets due to sensor positioning relief displacement (due to an inadequate DEM) the individual pixels representing a particular object or region on the ground will not coincide in the two images Problem is minimized if • Same sensor collected all data (e.g., Landsat 8) • Viewing conditions are similar • Sun angle • Viewing angle • Pushbroom (rather than whiskbroom) Presentation borrows heavily from: https://www.e-education.psu.edu/geog883/node/496 Change Detection • Atmospheric effects must be accounted for Atmospheric correction Atmospheric normalization Atmospheric correction of the individual images is not required, but normalization is essential. If classified images are to be compared, the categories (and labeling) must be the same in both images. Change Detection • Resolution of all types should be as similar as possible. Spatial: Change can only be associated reliably with the lowest spatial resolution image. Spectral: Spectral bands from different sensors typically have different characteristics even for the same spectral range (green, NIR, SWIR, …). There may be differences between images that are associated with the band selection even when there is no real change, (e.g., Landsat 5, 7, and 8). • Band center • Band width Radiometric: There can be significant differences introduced by differences in dynamic range and bit depth. Temporal: This refers to both time of day and time of year. • Sun angle (control for illumination effect, shadows, etc.) • Time of year to compare similar surface conditions • amount of foliage (leaf-on, leaf-off) • crop stage, etc.) • shadowing Change Detection Ideally, two images being compared should meet the following criteria (Campbell, 2011): • Acquired from the same sensor, or two sensors that have been rigorously inter-calibrated (such as two individual sensors from the same system of sensors) • Acquired at the same time of day using the same field of view and look angle • If of different years, acquired during the same season to minimize differences due to normal plant life cycles • Co-registered to within two-tenths of a pixel or less. • Cloud-free • Atmospherically corrected to surface reflectance • Free of other differences that are not part of the signal of interest (i.e., soil moisture content could be a distraction or a relevant signal depending on the application; normal forest harvest could be confused with trees downed in a storm). Change Detection Visual Inspection Visually comparing co-registered images from two dates is always the first place to start, even if the ultimate goal is to use an automated algorithm for classification or change detection (Campbell, 2011). Most image processing packages include tools to swipe one image over the other, flicker between images, and view images side-by-side. In some cases, heads up digitizing may be used to identify and classify change; in other cases, visual inspection is used to help select the most appropriate automated change detection technique. Change Detection https://www.youtube.com/watch?v=VklNwIRqkNU https://www.youtube.com/watch?v=c7lHVbJkDAw The seven change detection technique categories 1. Algebra Based Approach • • • • • image differencing image regression image ratioing vegetation index differencing change vector analysis 2. Transformation • PCA • Iterative Mutivariate Alteration Detection • Tasseled Cap (KT) • Gramm-Schmidt (GS) • Chi-Square 3. Classification Based • • • • • • Post-Classification Comparison Spectral-Temporal Combined Analysis EM Transformation Unsupervised Change Detection Hybrid Change Detection Artificial Neural Networks (ANN) 4. Advanced Models • Li-Strahler Reflectance Model • Spectral Mixture Model • Biophysical Parameter Method 5. GIS • Integrated GIS and RS Method • GIS Approach 6. visual Analysis • Visual Interpretation 7. Other Change Detection Techniques • • • • • • • • • Measures of spatial dependence Knowledge-based vision system Area production method Combination of three indicators: vegetation indices, land surface temperature, and spatial structure Change curves Generalized linear models Curve-theorem-based approach Structure-based approach Spatial statistics-based method https://www.slideshare.net/abhishek_bhatt/a-review-of-change-detection-techniques?from_action=save Algebra based Approaches • These algorithms have a common characteristic, i.e. selecting thresholds to determine the changed areas. • Simple, easy to implement and interpret, but cannot provide complete matrices of change information. • In this category, two aspects are critical for the change detection results: – selecting suitable image bands – selecting suitable thresholds Image Differencing • Pros – Simple – Easy to interpret • Cons: – Difference value is absolute, and the same value may have different meaning (e.g., 255-254 = 1-0 = 1) – Requires atmospheric correction or normalization Image Regression The relationship between pixel values on two dates is based on a regression function. The residuals are an indicator of where change occurred. • Pros – Reduces impact of atmospheric, sensor and environmental differences. • Cons: – Requires development of accurate regression functions. – Does not provide change matrix. Image Ratioing • Concept – Date 1 / Date 2 – No-change = 1 – Values less than and greater than 1 are interpretable – Pick a threshold for change • Pros – Simple – May mitigate problems with viewing conditions, esp. sun angle • Cons – Scales change according to a single date, so same change on the ground may have different score depending on direction of change; i.e. 50/100 = .5; 100/50 = 2.0