Abstract
Mixed pixels in medium spatial resolution imagery create major challenges in acquiring accurate pixel-based land use and land cover information. Deep belief network (DBN), which can provide joint probabilities in land use and land cover classification, may serve as an alternative tool to address this mixed pixel issue. Since DBN performs well in pixel-based classification and object-based identification, examining its performance in subpixel unmixing with medium spatial resolution multispectral image in urban environments would be of value. In this study, (1) we examined DBN’s ability in subpixel unmixing with Landsat imagery, (2) explored the best-fit parameter setting for the DBN model and (3) evaluated its performance by comparing DBN with random forest (RF), support vector machine (SVM) and multiple endmember spectral mixture analysis (MESMA). The results illustrated that (1) DBN performs well in subpixel unmixing with a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.0077. (2) A larger sample size (e.g., greater than 3000) can provide stable and high accuracy while two-RBM-layer and 50 batch sizes are the best parameters for DBN in this study. Epoch size and learning rate should be decided by specific applications since there is not a consistent pattern in our experiments. Finally, (3) DBN can provide comparable results compared to RF, SVM and MESMA. We concluded that DBN can be viewed as an alternative method for subpixel unmixing with Landsat imagery and this study provides references for other scholars to use DBN in subpixel unmixing in urban environments.
Highlights
Medium spatial resolution multispectral imageries, such as Landsat data, are widely used in geographical applications since they can cover large spatial areas and have a short repeat period
It is meaningful to examine the performance of Deep belief network (DBN) in subpixel unmixing with medium spatial resolution multispectral imagery, such as Landsat 5 Thematic Mapper (TM)
The objectives of this study are (1) to examine the performance of DBN in subpixel unmixing with Landsat imagery; (2) to explore the best-fit parameter setting for DBN; and (3) to examine DBN’s performance by comparing it with multiple endmember spectral mixture analysis (MESMA) [36], random forest (RF) [37,38,39] and support vector machine (SVM) [27,40,41]
Summary
Medium spatial resolution multispectral imageries, such as Landsat data, are widely used in geographical applications since they can cover large spatial areas and have a short repeat period. Mixed pixels, which contain more than one pure land cover class in a pixel, are inevitably found in medium spatial resolution imageries. Subpixel unmixing methods can provide more accurate results than traditional pixel-based classifications. Subpixel models, such as spectral mixture analysis [1], probabilistic model [2,3], geometric optical model [4,5], stochastic geometric model [6,7] and fuzzy analysis model [8,9], are commonly used to calculate the fractions or probabilities of all land cover classes in a mixed pixel
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