Automatic Urban Change Detection using LANDSAT-8 Satellite Images and Deep Learning Techniques in Australian Capital Territory (ACT), Australia

Abstract

The Remote Sensing based Satellite data is useful as a very powerful tools for providing a complete information for an accurate urban area monitoring. Identifying the urban change detection is a main part of professional urban drafting, regional based urban development in fraction of low cost effective along with less time duration compared to common methods (e.g. manually field survey report, Aerial Imagery etc.). The main goal of the proposed work is to monitoring various changes in features on the complete surface of earth of various pixel resolution can be carried out by low-cost remote sensing-based LANDSAT-8 satellite images.  The urban change detection is examining the various changes in urban area between two periods: 1) Baseline period 2) More recent period. In the previously, the classical change detection (CD-Algorithm) is inaccurately identifying both the spatial information and various scale variations within satellite images. To overcome the problem, the present research work introduces an advanced Deep Learning based novel Change Detection (Bitemporal_CD) technique that accurately extracts complete spatial information at various level of scales variations to simply address both issues. The new proposed model is based on Multi Scale and Multi Depth (MSMD) approach deep neural network that generates all binary information change based map that simply integrates with complete information of different sizes of patches at various decision parameter. It is used LANDSAT-8 satellites images originally from Australian Capital Territory (ACT), Suburban Nicholls, and District: Gungahlin- Australia, because total area of the ACT is 2,351.7 km2 of which 61% area is hilly or complete mountainous. The proposed advanced deep learning model is estimated in comparison with both Bi_Temporal Change Vector Analysis (Bi_Temporal CVA) and Support-based Vector Machine (SVM). On the basis of Deep learning change detection results, our proposed model shows a notable advancement in the performance of Cohen’s_kappa coefficient (KC) compared to both SVM as well as Bi_Temporal CVA, with high increases of approximately 12.87% and 30.37% individually. Finally, the proposed Multi Scale and Multi Depth approach deep neural network model performs superior in detecting various changes including across all level of metrics with high accuracy.