Remote sensing techniques provide efficient and cost-effective approach to monitor the expansion of built-up area, in comparison to other traditional approaches. For extracting built-up class, one of the common approaches is to use spectral and spatial features such as, Normalized Difference Built-up index (NDBI), GLCM texture, Gabor filters etc. However, it is observed that classes such as river soil and fallow land usually mix up with built-up class due to their close spectral similarity. Intermixing of classes have been observed in the classified image when using spectral channels. In this paper, an approach has been proposed which uses urban based spectral indices and textural features to extract built-up areas. Three well known spectral indices i.e. NDBI, Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBai) have been used in this work. Along with spectral indices, local spatial dependency of neighborhood regions is captured using eight GLCM based textural feature, such as, Contrast, Correlation, Energy and Homogeneity etc. for each image band. All textural and spectral indices bands are combined and used for extracting built-up areas using Support Vector Machine (SVM) classifier. Results suggest 4.91% increase in overall accuracy when using texture and spectral indices in comparison with 84.38% overall accuracy achieved when using spectral data only. It is observed that built-up class are more separable in the projected spectral-spatial feature space in comparison to spectral channels. Incorporation of textural features with spectral features reduces the misclassification error and provides results with less salt and pepper noise.

ABSTRACT:With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31% and 82.86% overall accuracies respectively. Inception-v3 and VGGNet gives 89.43% of overall accuracy using fine-tuned VGGNet and 92.10% when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

No abstract

No abstract

No abstract