A novel hybrid machine learning approach for change detection in remote sensing images

Pati, Chinmayee; Panda, Ashok Kumar; Tripathy, Ajaya Kumar; Pradhan, Sateesh Kumar; Patnaik, Srikanta

doi:10.1016/j.jestch.2020.01.002

Cited by 19 publications

(9 citation statements)

References 19 publications

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“…e authors have performed experimentation while using Mexico dataset and Sardinia image datasets. e results are validated with existing results and the proposed approach outperforms when compared to the existing results [56]. In early years of ML, the accuracy of only high spectral images was high [57].…”

Section: Machine Learningmentioning

confidence: 56%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

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Section: Machine Learningmentioning

confidence: 56%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…Another contribution of this research work is to compare the proposed model with other existing machine learning classifiers such as KNN, 23 NB, 23 DT, 23 LR, 24 RF, 24 and SVM 28 . From Figure 13, it is apparent that the proposed model outperforms well when compared with the existing machine learning classifiers.…”

Section: Resultsmentioning

confidence: 97%

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

Govardhan¹,

Surendranath

Sundararajan³

2022

Concurrency and Computation

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In recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)-based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant-sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5-fold cross-validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

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“…Pati et al automatically labeled data using clustering and fuzzy logic and used the labels to train a supervised model [46]. Other works detected building damage with an outlier detection deep autoencoder [10] and an anomaly detecting generative adversarial network [47].…”

Section: Semi-supervised Learning For Change Detectionmentioning

confidence: 99%

BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge

Ismail¹,

Awad²

2022

Preprint

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Change detection is instrumental to localize damage and understand destruction in disaster informatics. While convolutional neural networks are at the core of recent change detection solutions, we present in this work, BLDNet, a novel graph formulation for building damage change detection and enable learning relationships and representations from both local patterns and non-stationary neighborhoods. More specifically, we use graph convolutional networks to efficiently learn these features in a semisupervised framework with few annotated data. Additionally, BLDNet formulation allows for the injection of additional contextual building meta-features. We train and benchmark on the xBD dataset to validate the effectiveness of our approach. We also demonstrate on urban data from the 2020 Beirut Port Explosion that performance is improved by incorporating domain knowledge building meta-features.

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A novel hybrid machine learning approach for change detection in remote sensing images

Cited by 19 publications

References 19 publications

Remote Sensing Image Classification: A Comprehensive Review and Applications

Remote Sensing Image Classification: A Comprehensive Review and Applications

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge

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