My Account | Careers | Downloads | Blog

PROJECT TITLE :

Automatic Land Cover Reconstruction From Historical Aerial Images An Evaluation of Features Extraction and Classification Algorithms

ABSTRACT:

As large-scale epidemiological studies including retrospective study of spatial patterns have proliferated, the scientific community has become increasingly interested in land cover reconstruction from monochromatic historical aerial photographs. Remote sensing applications are, however, the primary focus of the computer vision community's work in the interpretation of high-resolution multispectral data collected by advanced spatial programmes. As a result, this study proposes four new ideas. They want to serve as a benchmark for future computer vision algorithms used to automate the restoration of land cover from black-and-white historical aerial photographs. An annotated map of France from 1970 to 1990 has been developed with the cooperation of geography specialists, which includes 4.9 million non-overlapping annotated patches. For this dataset, we've dubbed it HistAerial. A thorough comparison analysis of the current state of the art texture feature extraction and classification algorithms, including deep convolutional neural networks (DCNNs), has also been conducted. In the form of a rating, it's given An orthogonal combination representation of the binary gradient contours filter, known as the rotated-corner local binary pattern (R-CRLBP), is offered as a simplification of the binary gradient contours filter. To wrap things up, a novel mixture of low-dimensional texture descriptors, including the R-CRLBP filter, is introduced as a light combination of local binary patterns (LCoLBPs). In comparison with the DCNN techniques, the LCoLBP filter obtained state-of-the-art results on the HistAerial dataset with a low-dimensional feature vector space (17 times shorter).


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : T-PAIR Temporal Node-pair Embedding for Automatic Biomedical Hypothesis Generation ABSTRACT: In this paper, we investigate a problem known as automatic hypothesis generation (HG). HG refers to the process of discovering
PROJECT TITLE : Millimeter-Wave Mobile Sensing and Environment Mapping Models, Algorithms and Validation ABSTRACT: One relevant research paradigm, particularly at mm-wave and sub-THz bands, is to integrate efficient connectivity,
PROJECT TITLE : End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning ABSTRACT: Objective. The mean intracranial pressure, also known as ICP, is a measurement that is frequently
PROJECT TITLE : End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning ABSTRACT: Objective. The mean intracranial pressure, also known as ICP, is a measurement that is
PROJECT TITLE : Attention in Reasoning Dataset, Analysis, and Modeling ABSTRACT: Although attention has become an increasingly popular component in deep neural networks for the purpose of both interpreting data and improving

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry