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Advancements in AI-Based Remote Sensing Object Detection

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Dear Colleagues,

In recent years, it has become possible to perform not only high-frequency observations using geostationary satellites but also remote sensing using small satellite constellations and drones. To perform monitoring using these, it is necessary to extract the changes and desired information from a large amount of data. Additionally, when photographing from above, the view is from above rather than from the side as we normally would, making visual inspections difficult. Remote sensing object detection is important for these reasons.

Meanwhile, applications of deep learning are popular in generic image recognition, although it is necessary to determine the features and their thresholds used for image recognition; for example, supervised deep learning models learn and determine them from training images. In the results, it is inferred that the features that have been difficult to formulate are also being used.

Although the aim of this Special Issue is AI-based remote sensing object detection, detection using classification models and segmentation models, change detection, and tracking are also acceptable. Moreover, accompanying technologies and applications are similarly welcome. This Special Issue welcomes techniques and experimental research articles on the following topics, although progress reports on relevant research issues are also acceptable.

Dr. Yu Oishi
Dr. Daniele Pannone
Guest Editors

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Keywords

deep learning
image recognition
change detection
object detection
tracking
monitoring
new sensors
algorithms
applications

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18 pages, 12643 KiB

Open AccessArticle

Detecting Moving Wildlife Using the Time Difference between Two Thermal Airborne Images

by Yu Oishi, Natsuki Yoshida and Hiroyuki Oguma

Remote Sens. 2024, 16(8), 1439; https://doi.org/10.3390/rs16081439 - 18 Apr 2024

Viewed by 329

Abstract

Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been confirmed. To date, the use of visible and near-infrared images has been limited to the daytime because many large mammals, such as sika deer (Cervus nippon), are crepuscular. However, it is difficult to detect wildlife in the thermal images of urban areas that are not open and contain various heat spots. To address this issue, a method was developed in a previous study to detect moving wildlife using pairs of time-difference thermal images. However, the user’s accuracy was low. In the current study, two methods are proposed for extracting moving wildlife using pairs of airborne thermal images and deep learning models. The first method was to judge grid areas with wildlife using a deep learning classification model. The second method detected each wildlife species using a deep learning object detection model. The proposed methods were then applied to pairs of airborne thermal images. The classification test accuracies of “with deer” and “without deer” were >85% and >95%, respectively. The average precision of detection, precision, and recall were >85%. This indicates that the proposed methods are practically accurate for monitoring changes in wildlife populations and can reduce the person-hours required to monitor a large number of thermal remote-sensing images. Therefore, efforts should be made to put these materials to practical use. Full article

(This article belongs to the Special Issue Advancements in AI-Based Remote Sensing Object Detection)

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