The image classification accuracy is enhanced by applying Deep Learn ing Models (DLM) which has a robus t learning ability by incorporating both feature extract ion and classification p rocedure into single image classificat ion test.... more
The image classification accuracy is enhanced by applying Deep Learn ing Models (DLM) which has a robus t learning ability by incorporating both feature extract ion and classification p rocedure into single image classificat ion test. Here a deep learn in g-based classification technique is applied to High Spatial Resolution Remote Sensing Images (HSRRSI) to ext ract mult i-layer features. The two networks i.e., residual network and inception network are co mb ined into one new model to obtain higher accuracy then said individual residual network and inception network. The new model designed was extensively weighed on data's from Remote Sensing Image Classificat ion Benchmark (RSI-CB). The dataset obtained from RSI-CB is split into 70:30 rat io for training and testing respectively. The performances of proposed approach are then assessed by kappa coefficient (K) and accuracy (A).
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Economic development and growth in population have prompted rapid changes to earth's land cover over the last few decades, and there is every indication that the pace of these changes will accelerate in the future. Therefore, systematic... more
Economic development and growth in population have prompted rapid changes to earth's land cover over the last few decades, and there is every indication that the pace of these changes will accelerate in the future. Therefore, systematic evaluations of Earth's land cover must be repeated at a frequency that allows monitoring of both long term trends as well as inter-annual variability, and at a level of spatial detail to allow study of land use patterns. Land cover analysis can be done most effectively through remote sensing images of various spatial, spectral and temporal resolutions to improve the selection of areas designed for agricultural, urban and/or industrial areas of a region. Astute efforts have been made in developing advanced classification algorithms and techniques for improving the accuracy of land cover classification. Recent image classification approaches for land cover pattern analysis have been brought together with their pros and cones by reviewing literatures, books, manuals and other related documents. Suitable classification algorithms may be chosen based on their performance, type of image and application area.