Abstract
Classification of aerial photographs relying purely on spectral content is a challenging topic in remote sensing. A convolutional neural network (CNN) was developed to classify aerial photographs into seven land cover classes such as building, grassland, dense vegetation, waterbody, barren land, road, and shadow. The classifier utilized spectral and spatial contents of the data to maximize the accuracy of the classification process. CNN was trained from scratch with manually created ground truth samples. The architecture of the network comprised of a single convolution layer of 32 filters and a kernel size of 3 × 3, pooling size of 2 × 2, batch normalization, dropout, and a dense layer with Softmax activation. The design of the architecture and its hyperparameters were selected via sensitivity analysis and validation accuracy. The results showed that the proposed model could be effective for classifying the aerial photographs. The overall accuracy and Kappa coefficient of the best model were 0.973 and 0.967, respectively. In addition, the sensitivity analysis suggested that the use of dropout and batch normalization technique in CNN is essential to improve the generalization performance of the model. The CNN model without the techniques above achieved the worse performance, with an overall accuracy and Kappa of 0.932 and 0.922, respectively. This research shows that CNN-based models are robust for land cover classification using aerial photographs. However, the architecture and hyperparameters of these models should be carefully selected and optimized.
Highlights
Classifying remote sensing data (especially orthophotos of three bands—red, green, blue (RGB)) with traditional methods is a challenge even though some methods in literature have produced excellent results [1, 2]
The results showed that convolutional neural network (CNN) is very effective in learning discriminative contextual features leading to accurate classified maps and outperforming traditional classification methods based on the extraction of textural features
This study has proposed a classification method that is based on CNN and spectral-spatial feature learning for classifying very high-resolution aerial orthophotos
Summary
Classifying remote sensing data (especially orthophotos of three bands—red, green, blue (RGB)) with traditional methods is a challenge even though some methods in literature have produced excellent results [1, 2]. The advantages of deep learning methods include learning highorder features from the data that are often useful than the raw pixels for classifying the image into some predefined labels. There are several methods and algorithms that have been adopted by many researchers to efficiently classify a very high-resolution aerial photo and produce accurate land cover maps Methods such as object-based image analysis (or OBIA) was mostly investigated because of its advantage in very high-resolution image processing via spectral and spatial features. Vogels et al [8] combined OBIA with random forest classification with texture, slope, shape, neighbor, and spectral information to produce classification maps for agricultural areas They have tested their algorithm on two datasets, and the results showed the employed methodology
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
