Introduction: Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service delivery, and general urban and rural planning and management. Whereas deep learning approaches have recently gained popularity in remotely sensed image analysis, they have been unable to efficiently detect image objects due to complex landscape heterogeneity, high inter-class similarity and intra-class diversity, and difficulty in acquiring suitable training data that represents the complexities, among others. Methods: To address these challenges, this study employed multi-object detection deep learning algorithms with a transfer learning approach on remotely sensed satellite imagery captured on a heterogeneous landscape. In the study, a new dataset of diverse features with five object classes collected from Google Earth Engine in various locations in southern KwaZulu-Natal province in South Africa was used to evaluate the models. The dataset images were characterized with objects that have varying sizes and resolutions. Five (5) object detection methods based on R-CNN and YOLO architectures were investigated via experiments on our newly created dataset. Conclusions: This paper provides a comprehensive performance evaluation and analysis of the recent deep learning-based object detection methods for detecting objects in high-resolution remote sensing satellite images. The models were also evaluated on two publicly available datasets: Visdron and PASCAL VOC2007. Results showed that the highest detection accuracy of the vegetation and swimming pool instances was more than 90%, and the fastest detection speed 0.2 ms was observed in YOLOv8.

/pdf/state-of-the-art-deep-learning-methods-for-objects-detection-s9xrazg0.pdf

State-of-the-Art Deep Learning Methods for Objects Detection in Remote Sensing Satellite Images

&lt;p&gt;In this modern age, several new methods have been developed, especially in image processing for agriculture business, which consists of technologies derived from artificial intelligence (AI) capabilities called machine learning. Classify is a widely used method to analyze patterns, trends, as well as the body of knowledge from the data visualization. Image classification application improves discrimination and prediction efficiency. The objective of this research was to feature extraction of sweet tamarind and compare the algorithm for classification. This research used images from golden sweet tamarind species with the use of MATLAB and Python language. The steps of this research consisted of 1) preprocessing step for finding the distance to appropriate of the image quality, 2) feature extracting for finding the number of black pixels and the number of white pixels, perimeter, diameter, and centroid, and 3) classifying for algorithms' comparison. The results showed that the camera's distance to the image was 60 cm. The coefficient of determination was at 0.9956, and the Standard Error of Estimate was 7,424.736 pixels. The conclusion of classification found that the random forest had the highest accuracy at 92.00%, SD. = 8.06, precision = 90.12, recall = 92.86, and F1-score = 91.36.&lt;/p&gt; 

Feature extraction to predict quality of segregating sweet tamarind using image processing

Quality of data is the key for machine learning. In the case of sensitive data, de-identification must be adopted to guarantee data privacy. Supervisory organizations have enacted regulations, e.g., EU GDPR, to govern these behaviors. It makes the reliability of machine learning become a critical challenge as de-identification must lead to the information loss. Previous algorithms (such as k anonymity) only focused on optimizing metrics of anonymity. They do not consider requirements of machine learning and may degrade performance from conducted results. This paper adjusts k-anonymity algorithms by feature importance (FI) and margin preservation (MP) of data classes to resolve this drawback. FI was explored to have a high-degree correlation to machine learning performance. MP not only anonymizes data but also tries to preserve original class margins of data. FI and MP schemes are tested on 4 different k-anonymity algorithms with different values of k. Four different datasets are applied to evaluate the performance for three machine learning models. These comprehensive experiments demonstrate that our schemes can improve machine learning of k-anonymity data. Comparing to the original k-anonymity algorithms, the best improvement of our improved k-anonymity algorithms with FI and MP can lead to over 10% and 17% in f1score, respectively.

Machine Learning of k-Anonymity Data by using Feature Importance and Margin Preservation

Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.

https://www.mdpi.com/2073-8994/12/10/1619/pdf

Context-Dependent Object Proposal and Recognition

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Context-Dependent Object Proposal and Recognition