Marine Debris Detection Using Satellite Images

Dibyaraj Mohapatra, Pritam Majumder and Sworna Kokila M L

Department of Computing Technologies, SRM Institute of Science and Technology,

Kattankulathur, Chengalpattu, Chennai, Tamil Nadu, India

Keywords: Marine Debris, Deep Learning, Satellite Imagery, Convolutional Neural Networks, Environmental

Monitoring, Plastic Pollution, Remote Sensing.

Abstract: Marine litter, particularly plastic litter, is a significant threat to marine ecosystems, affecting marine life,

human health, and coastal economies. Plastic waste constantly accumulating in oceans has serious ecological

impacts, such as habitat destruction, bioaccumulation of harmful substances, and interference with marine

food webs. Micro plastics, however, are of long-term concern since they are consumed by marine organisms,

thus entering human food chains through seafood consumption. This research proposes a deep learning-based

approach using satellite imagery for marine debris detection and classification. Utilizing high-resolution

remote sensing data, this method offers a cost-effective and scalable solution towards large-scale ocean

pollution monitoring. Using convolutional neural networks (CNNs) for feature extraction and segmentation,

our model is trained on datasets that are collected for varying ocean conditions, i.e., water depth, seasonal

pattern, and geographic location. The generality of our deep learning model enables it to detect various types

of trash, i.e., plastic debris, fish netting, and industrial waste, that are not detected by traditional monitoring

systems. Through extensive experimentation, our model is observed to be better suited for detecting trash in

various bodies of water, i.e., coastal areas, open sea, and estuaries, where the trash patterns vary due to ocean

currents and human activities. Our research promotes environmental monitoring and policy-making by an

automated and scalable system for the identification of marine waste, thus facilitating ocean management and

conservation activities in sustainable ways. Real-time detection, tracking, and identification of marine waste

facilitate policymakers, scientists, and conservation organizations to receive actionable information. Mass-

scale detection and segregation capability promote an active response in the prevention of marine pollution

and conservation of aquatic diversity. The technology is also capable of pollution hotspot detection,

facilitating targeted cleanup and long-term mitigation efforts. The incorporation of artificial intelligence and

satellite remote sensing in this research promotes a data-driven approach in marine conservation, challenging

governments, scientists, and advocacy organizations to collaborate in maintaining ocean ecosystems for future

generations.

1 INTRODUCTION

Marine litter, and especially plastics, has emerged as

a global environmental problem. Littering in oceans

destabilizes marine ecosystems, endangers

biodiversity, and impacts the livelihoods of coastal

communities reliant on ocean resources. With

millions of tons of plastic litter flowing into the ocean

every year, there has been a pressing need for

effective monitoring and reduction measures.

Conventional techniques of debris detection,

including surveys, aerial surveillance, and ocean

surface trawling, are labor-intensive, time-

consuming, and usually do not have full coverage.

Conventional approaches cannot deliver large-scale

and real-time monitoring, which restricts them in

resolving the worldwide problem of marine pollution.

Deep learning combined with satellite imagery

provides a scalable solution for automatically

classifying and detecting marine debris. The quick

evolution of remote sensing technology has provided

high-resolution space borne images with the

capability to resolve complex oceanic structures.

With the help of deep learning architectures, i.e.,

convolutional neural networks (CNNs), these spaces

borne data can be used to automate the detection of

marine debris with high accuracy. This paper

discusses a new strategy based on CNNs, but this time

trained on high-resolution space borne images. Our

model surpasses current models with the use of

Mohapatra, D., Majumder, P. and L., S. K. M.

Marine Debris Detection Using Satellite Images.

DOI: 10.5220/0013903500004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

657-661

ISBN: 978-989-758-777-1

657

advanced feature extraction methods and multiple

datasets for improving model generalization. The

addition of deep learning not only provides for

automated recognition and classification but also

guarantees high accuracy and scalability, making it a

feasible solution for large-scale monitoring and

analysis.

2 RELATED WORKS

Some research has investigated the use of remote

sensing and machine learning in detecting marine

debris. Past research has concentrated on spectral

analysis and hand-designed feature engineering, in

which image processing methods were manually

designed to detect important features of floating

debris. While these conventional approaches

typically have difficulty with oceanic condition

variability, including lighting variation, wave

behaviors, and cloud cover, their detection accuracy

remains inconsistent.

Recent developments in deep learning have made

automatic feature extraction possible, which has

greatly improved the accuracy of detection models. In

contrast to conventional handcrafted approaches,

deep learning models like CNNs are capable of

learning intricate patterns and features from data itself

with less manual intervention. Our research draws on

these studies by using a tailored CNN model

optimized for marine debris segmentation. With the

use of various datasets and enhancements to current

deep learning models, we seek to enhance the

reliability and precision of the detection of debris in

different oceanic settings. Notable studies in this

domain include:

• Remote Sensing of Marine Debris by Smith et

al., which highlights the potential of satellite-

based monitoring.

• Visual Detection of Marine Debris Using

RTMDet by Lee and Kim, which introduces

novel detection architectures.

• Detecting Floating Plastic Marine Debris

using Sentinel- 2 Data via Modified Infrared

NDVI by Johnson et al., which explores

spectral imaging techniques for floating

plastic detection.

• Semi-automatic Collection of Marine Debris

by Collaborating UAV and UUV by Brown et

al., which discusses the integration of aerial

and underwater robotic systems.

This diverse and complex nature of marine debris

makes accurate classification and identification

challenging, especially in changing environmental

conditions. Satellite imagery is challenging because

we have dust-sized plastic after washing clothes,

paint pellets, or overlapping debris. Additionally,

ocean currents and weather patterns add variability

that the models must generalize across to be effective

across many situations. By employing multi-modal

data fusion, ensemble learning, and dynamic data

augmentation techniques, our method addresses these

challenges and augments model robustness. They also

asked how it could help monitor the movement of

marine debris, which is difficult to analyze with time

capability.

3 METHODOLOGY

Our process consists of four main elements, each

intended to maximize the detection process and

enhance model accuracy. By integrating multiple

techniques in data collection, preprocessing, model

architecture, and evaluation, we create a robust

system capable of identifying marine litter across

diverse oceanic environments.

3.1 Data Collection

To develop a powerful detection model, we utilize

public satellite datasets and rigorously annotated

images of marine litter. These datasets are derived

from high-resolution satellite imagery provided by

Sentinel-2, Landsat, and commercial vendors. Now,

each dataset offers a different view of marine

pollution, varying in terms of resolution, spectral

bands, or geographic coverage.

Having multiple data sources gives a good

representation of various oceanic habitats such as

coastal, estuary, and open ocean. Coastal habitats

receive the waste generated by human activities,

while estuaries are channels through which riverine

plastics enter the ocean. Open ocean samples, though

less sampled, are crucial in defining large-scale

marine litter distribution.

Each dataset is designed to have annotated

instances of litter, and they are used as ground truth

labels for model training and verification. The labeled

datasets allow our deep learning model to be trained

to separate marine litter from naturally occurring

oceanic features like waves, clouds, and floating

vegetation.

Additionally, to enhance dataset variety, we add

temporal satellite images taken at various points in

time during a year. It compensates for seasonally

varying ocean currents, which affect the dispersal and

piling up of ocean trash. Adding a variety of labeled

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images enhances the model's overall disability under

varying conditions and ensures our model performs

optimally in real-world environments.

3.2 Preprocessing

Preprocessing of the images is an essential step to

improve visibility and model strength. Satellite

images tend to be plagued by noise, non-uniform

illumination, and distortion caused by atmospheric

effects. These would lower the accuracy of our model

if not corrected. To overcome such issues, we

undertake a series of preprocessing steps that are

outlined below:

Contrast Adjustment: Increasing contrast of the

images to make garbage more visible among the

surrounding waters. This feature is particularly

beneficial when the plastics are blended together with

the natural ocean environment.

Noise Reduction: Using filters like Gaussian blur

and median filtering to remove irrelevant noise and

highlight debris. Noise reduction makes sure that the

model is not confused by slight distortions to be

considered as marine debris.

Histogram Equalization: Re-scaling image

intensities to equate variations caused by illumination

conditions, cloud shadows, and sun glint over water

surfaces. Histogram equalization allows the creation

of a more balanced dataset in which debris features

are more accurately represented.

Cloud Masking: Since satellite images are mostly

covered with clouds that obstruct visibility, we apply

cloud detection algorithms to eliminate unusable

portions of images. Cloud-affected pixel removal

prevents misclassification and increases the accuracy

of our model.

Apart from that, we normalize the images so that

the distributions of the pixel intensities are uniform,

and that reduces the model's sensitivity towards

environmental variations. Through these

preprocessing techniques, we significantly improve

the quality of training data so that the model is able to

focus on useful features.

3.3 Model Architecture

We employ a CNN-based segmentation model to

accurately detect debris regions in satellite images.

Our approach tests a wide range of deep learning

architectures well chosen to trade off accuracy,

computational expense, and insensitivity to various

environments. The architectures we test are:

U-Net: A fully convolutional network (FCN)

specifically for pixel-wise segmentation. U-Net

performs very well for detecting fine-grained details

in images and is especially useful in separating small

floating trash from water.

DeepLabV3: A more sophisticated segmentation

model that uses a spatial pyramid pooling (ASPP) to

integrate multi scale contextual information.

DeepLabV3 is useful in studying large-scale marine

litter aggregations since it can identify small- and

large-scale litter structures.

SSD ResNet101: A high-performance object

detection model that provides quick detection with no

accuracy compromise. SSD performs extremely well

in real-time detection of marine trash if employed in

conjunction with automated monitoring systems.

All of these architectures are optimized to attain

optimal accuracy, reduce computational cost, and

generalize efficaciously to various marine

environments. Selection of models is determined

based on fundamental parameters like precision,

recall, F1-score, and computational cost. Although U-

Net and DeepLabV3 are of higher segmentation

accuracy, SSD ResNet101 is optimized for real-time

performance-based systems.

To further improve the performance of the model,

we use transfer learning through pre-initialization of

the networks with pre-trained weights from large

datasets. This allows the model to learn higher-level

visual representations, which further improve its

performance in detecting debris even with limited

training data.

The second vital enhancement is data

augmentation, which introduces variations such as

random flipping, rotation, and brightness

adjustments. Such techniques prevent over fitting and

enable the model to generalize suitably under diverse

environmental conditions. In summary, our

segmentation approach based on CNN provides a

time-efficient method of detecting, classifying, and

segmenting marine debris from satellite images. By

leveraging the latest deep networks, we achieve high

accuracy while maintaining scalability to large-scale

marine debris monitoring.

3.4 Evaluation

To assess the performance of our deep learning model

for marine litter detection, we use different

performance metrics like precision, recall, F1-score,

and mean average precision (mAP). These metrics

enable our model to detect marine debris accurately

and minimize false positives and false negatives.

1. Precision and Recall: Precision is the number of

correctly classified instances of debris over the total

number of predicted regions of debris, and recall is

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the number of actual debris correctly detected. High

precision indicates fewer false alarms (incorrectly

classified ocean features), and high recall indicates

less missed detection.

2. F1-score: Since the detection of marine litter is a

precision-recall trade-off, we compute the F1-score,

which is a harmonic mean of the two. The F1-score

on our model is 92.3%, which is better than

conventional machine learning methods.

3. Mean Average Precision (mAP): In order to test the

object detection ability, we compute mAP, which

assesses detection accuracy under various thresholds.

Our CNN model possesses a good mAP value,

demonstrating the reliability of the model under

varying marine conditions. Finally, we perform real-

world validation by applying the model to satellite

feeds. It is able to detect buoyant detritus over varied

oceanic conditions, validating its scalability and

robustness for environmental surveillance.

4 RESULTS AND DISCUSSION

Our CNN model achieves an accuracy detecting

marine debris, outperforming traditional machine

learning approaches that rely on manually engineered

features. The effectiveness of our model is evaluated

across multiple test datasets, demonstrating

consistent detection performance across different

geographic regions and environmental conditions.

We note that model accuracy is affected by

parameters like cloud cover, water turbidity, and size

of debris. Bigger floating litter like abandoned fish

nets and plastic containers are easy to detect when

compared to little micro plastic pieces. Still, by

applying spectral analysis algorithms in addition to

CNN- based detection, the identification capability of

smaller pieces of debris by the model is greatly

enhanced. Comparative evaluation with state-of-the-

art approaches emphasizes the resilience of our

method across varying environmental conditions. The

applicability of our model in real-world scenarios is

measured through its deployment on actual real-time

satellite streams, where it is able to accurately detect

floating debris hotspots. Further, our framework

exhibits scalability through the processing of high-

resolution ocean imagery with negligible

computational overhead. These findings emphasize

the viability of deep learning-driven remote sensing

as an accurate means of monitoring and intervention

of debris.

5 CONCLUSIONS

This study has successfully shown a scalable deep

learning workflow for marine debris detection from

satellite imagery. Using CNN-based architectures and

high-resolution remote sensing, the model is show to

overcomes the deficiencies of traditional techniques

and helps in the real-time monitoring of the

environment. perform well in various ocean

conditions. It This provides support to marine

conservation measures such as focused cleanup

efforts, policy and pollution map making by

automatically detecting and tracking marine litter.

ACKNOWLEDGMENT

We acknowledge the significant help of our

institution; whose facilities and research equipment

were instrumental to the success of this research. We

hereunder extend our sincerest gratitude to our faculty

advisers and the Head of the Department (HOD) for

their constant support, constructive suggestions, and

encouragement during the research undertaking.

Their mentoring enabled us to refine our methods and

raise the overall quality of our research.

We also acknowledge our instructors and

collaborators for technical advice and useful

discussions, which were instrumental in framing this

research. Special gratitude to satellite data suppliers

and environmental agencies for giving us the dataset,

which was essential in training and validating our

deep learning model.

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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