Unsupervised Change Detection in Remote Sensing Images Using CNN Based Transfer Learning

Change detection (CD) using remote sensing images have gained much attention in recent past due to its diverse applications. Devising reliable CD techniques that integrate huge topographical information is highly challenging. Researches in deep learning paradigm, particularly with Convolutional neural networks (CNN), have proven that CNN are efficient in abstracting knowledge from mul- tiple spectral bands, easy to be trained, and capable of deriving inference from unseen datasets. However, gathering training patterns are difficult in many real life problems and therefore, the pre-trained CNN models can be applied effectively. Hence, we consider three CNN models, VGG19, InceptionV3 and ResNet50 for feature extraction using transfer learning, followed by KMeans and Fuzzy C-Means(FCM) clustering algorithms for generating change maps. The proposed methods have been tested on two representative datasets of different land cover dynamics and have exhibited promising results with high overall accuracy and Kappa statistic (95.09 & 0.8173 respectively on Dubai city dataset and 97.12 & 0.8970 respectively on Texas dataset for Resnet+FCM) as well as superior to the state-of-the-art methods compared.