Classification Of Damaged Vegetation Areas Using Convolutional Neural Network Over Unlabelled Sentinel-2 Images

Haque, Samiha; Rahman, Nazibur; Mostakim, Moin

doi:10.23919/icac50006.2021.9594269

Cited by 3 publications

(1 citation statement)

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“…These models allow for lower complexity classifications via use of simplified feature extraction & reduction techniques. Similar Machine learning Models are discussed in [25,26], for brown planthopper damage detection, and damaged vegetation areas classification applications. It is observed that these models are highly application specific, which limits their scalability for multiple application scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning for crop analysis

Dhande

Malik

2022

Preprint

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Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of satellite image processing models are proposed by researchers, and each of them have different data & process requirements. For instance, image capturing module Might obtain Images in layered form, while feature extraction module might require data in 2D or 3D forms. Moreover, performance of these models also varies due to changes in internal process parameters & dataset parameters, which limits their accuracy and scalability when applied to real-time scenarios. To reduce the probability of these limitations, a novel high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning is proposed & discussed in this text. The model initially captures real-time satellite data using Google’s Earth Engine and processes it using a transfer learning-based convolutional neural network (CNN) via backscatter coefficient analysis. These coefficients indicate average intensity value of Precision Image (PRI) when evaluated over a distributed target. Due to extraction of backscattering coefficients, the model is capable of representing crop images in VV (vertical transmit, vertical receive), and HV (horizontal transmit vertical receive) modes. Thereby assisting the CNN model to extract a wide variety of features from input satellite image, which classifies these datasets (original, VV, and VH) into different crop categories. The classified images are further processed via an incremental learning layer, which assists in visual identification of affected regions. Due to use of incremental learning and CNN for classification, the proposed TRSAITL model is capable of achieving an average accuracy of 97.8% for crop type & severity of damage detection, with an average PSNR of 29.6 dB for different image types. The model was tested on different regions around our local geographical area, and consistent performance was observed. This performance was also compared with various state-of-the-art approaches, and it was observed that the proposed TRSATL model has 5% better accuracy, 4.6% better precision, and 7.9% better recall when compared with them, which makes it highly useful for real-time satellite-based crop classification applications.

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Section: Literature Reviewmentioning

confidence: 99%