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Remarkable improvement has been made in object detection and image classification, mainly due to the availability of large-scale labelled data and also the progress of deep convolutional neural networks (CNNs). Thus, this amount of training data enables CNNs to learn data-driven image features. However, generating the efficient sample patches from the satellite images for training the CNNs remains a challenge. In this study, we use a CNN for the case of landslide detection based on the optical data from the Rapid Eye satellite. We separate the image into training and test areas of the highly landslide-prone Rasuwa district in Nepal. Thus, the sample patches were extracted from the training area of the Rapid Eye image. Although the approach of random sample patches is considered as the most common for feeding the CNNs, it is not the best solution for all object detection aims. We feed our structured CNN with the randomly selected sample patches as our first approach. For the second approach, the same CNN architecture is trained by the patches that selected based on only the central areas of any landslide. The trained CNNs based on both approaches were used to detection the landslides in an area where considered as our test zone. The detection results are compared against a precise inventory dataset of landslide polygons through a mean intersection-over-union (mIOU). The mIOU value of the first approach is 53.56%. However, that of the second one is 56.24%, which shows an approximately 3% improvement in the resulting accuracy of the landslide detection using the sample patches generated by the second approach. Rather, the current performance of CNNs in object detection domain they strongly depend on the quality of the training data and augmentation strategies.

Optimizing Sample Patches Selection of CNN to Improve the mIOU on Landslide Detection