A convolutional neural network architecture with multi-channel feature extraction for robustly classifying house types

This paper describes a robust convolutional neural network deep-learning architecture involving a multi-layer feature extraction for classification of house types. Previous studies regarding this type of classification show that this type of classification is not simple and most classifier models from the literature have shown a relatively low performance. For finding a suitable model, several similar classification models based on convolutional neural network have been explored. We have found out that adding better and more complex features do results in a significant accuracy related performance improvement. Therefore, a new model taking this finding into consideration has been developed, tested and validated. For training, testing and verification purposes of the developed model, various house images extracted from the Internet have been used. The test results clearly demonstrate and validate the effectiveness of the developed deep-learning model.