|Title||Understanding angular effects in VHR imagery and their significance for urban land-cover model portability : A study of two multi-angle in-track image sequences|
|Author(s)||Matasci, Giona; Longbotham, Nathan; Pacifici, Fabio; Kanevski, Mikhail; Tuia, Devis|
|Source||ISPRS Journal of Photogrammetry and Remote Sensing 107 (2015). - ISSN 0924-2716 - p. 99 - 111.|
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Atmospheric compensation - Domain adaptation - Histogram matching - Image classification - Maximum Mean Discrepancy - Multi-angle acquisitions|
This paper investigates the angular effects causing spectral distortions in multi-angle remote sensing imagery. We study two WorldView-2 multispectral in-track sequences acquired over the cities of Atlanta, USA, and Rio de Janeiro, Brazil, consisting of 13 and 20 co-located images, respectively. The sequences possess off-nadir acquisition angles up to 47.5° and bear markedly different sun-satellite configurations with respect to each other. Both scenes comprise classic urban structures such as buildings of different size, road networks, and parks. First, we quantify the degree of distortion affecting the sequences by means of a non-linear measure of distance between probability distributions, the Maximum Mean Discrepancy. Second, we assess the ability of a classification model trained on an image acquired at a certain view angle to predict the land-cover of all the other images in the sequence. The portability across the sequence is investigated for supervised classifiers of different nature by analyzing the evolution of the classification accuracy with respect to the off-nadir look angle. For both datasets, the effectiveness of physically- and statistically-based normalization methods in obtaining angle-invariant data spaces is compared and synergies are discussed. The empirical results indicate that, after a suitable normalization (histogram matching, atmospheric compensation), the loss in classification accuracy when using a model trained on the near-nadir image to classify the most off-nadir acquisitions can be reduced to as little as 0.06 (Atlanta) or 0.03 (Rio de Janeiro) Kappa points when using a SVM classifier.