Large-Scale Geospatial Data Analysis: Geographic Object-based Scene Classification in Remote Sensing Images by GIS and Deep Residual Learning

Abstract

Recent advances in optical sensor technologies and those of Geoinformatics can now support very large scale high definition multispectral and panchromatic images. This capability allows the use of remote sensing for the observation of complex ecosystems on earth. It supports sustaining biodiversity, precision agriculture, the management of land, crops, and parasites. It also supports advanced quantitative studies of biophysical and biogeochemical cycles in coastal or inland waters. One of the key applications that will allow the development of new types of decision support systems, is that of precise and effective scene classification. This will offer considerable advantages to business, science, and engineering. This work proposes a novel and very effective system based on geographic object-based scene classification in remote sensing images. More specifically the proposal includes a deep systems architecture of the Residual Neural Network (ResNet) type. This, omitting some layers in the early stages of training, achieves an effective simplification of the network by eliminating the Vanishing Gradient Problem (VGP) which troubles other deep learning architectures. Furthermore, the proposed system uses the softmax activation instead of the sigmoid function in the last level and the fully residual network was trained using the novel AdaBound algorithm that employs dynamic bounds on their learning rates. This achieves a smooth transition to stochastic gradient descent, tackling the noise dispersed points of misclassification with great precision. This is something that other spectral classification methods cannot handle. The proposed system has been tested, very successfully, in scene identification from remote sensing images. This confirms that it could be further used in advanced level processes for Large-Scale Geospatial Data Analysis such as cross-border classification, the recognition, and monitoring of certain patterns and multisensor data fusion.