Vegetation Analysis and Land Cover and Crop Types Classification of Granite Quarry Area of Dharmapuri and Krishna Giri Districts of Tamil Nadu
P.Nithya1, G. Arulselvi2
1P.Nithya, Research Scholar Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar (Tamil Nadu), India.
2Dr. G. Arulselvi, Asst. Professor, Research Supervisor, Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2670-2678 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7802068519/19©BEIESP
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Abstract: Deep Learning (DL) constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture and their allied services. The study mentioned that the aspect and altitude influenced the forest types and vegetation pattern. Deep learning (DL) is a powerful state-of-the-art technique for image processing including Remote Sensing (RS)images. This letter describes a multilevel deep learning (DL) architecture that targets land cover and crop type classification and detection from multitemporal multisource satellite imagery. The ubiquitous and wide applications like scene image understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applications, visual recognition systems which encompasses image classification, localization and detection have achieved great research now. Due to significant development in neural networks especially deep learning, these visual recognition systems have reached remarkable performance. Object detection is one of these domains witnessing great success in computer vision. This research paper demystifies the role of deep learning techniques based on Convolutional Neural Network(CNN) for object detection. Deep learning frameworks and services available for object detection are also enunciated. Deep learning techniques for state-of-the-art object detection systems are assessed in this research paper. Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by LANDSAT-8 and SENTINEL-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular Teak and Sugarcane, and yielding the target accuracies more than 85% for all major crops in Tamilnadu(Banana Tree, Teak, Paddy, and Sugarcane).
Keywords: Vegetation, ArcGIS, Land Use Crop Type, Geographic Information System (GIS), Remote Sensing (RS), Images Classification, Deep Learning(DL).
Scope of the Article: Deep Learning