Skip to content

Computer Vision Pipeline for Aerospace Industry

Use Case: Real-time Anomaly Detection in Satellite Imagery

Problem: Real-time detection of anomalies (e.g., wildfires, deforestation) in satellite images to facilitate quick response.

Pipeline Overview:

graph LR;
    A[Image Acquisition] --> B[Preprocessing];
    B --> C[Feature Extraction];
    C --> D[Anomaly Detection];
    D --> E[Alert System];
    E --> F[Data Archival & Reporting];

Description

  1. Image Acquisition: Continuous capture of satellite images with multispectral or hyperspectral cameras.

  2. Preprocessing: Georeference images, apply atmospheric corrections, and enhance specific spectral bands for anomaly detection.

  3. Feature Extraction: Extract key features like temperature gradients, vegetation indices (e.g., NDVI), and texture patterns that highlight anomalies.

  4. Anomaly Detection: Use a convolutional neural network (CNN) or a deep anomaly detection model to identify irregular patterns, such as wildfires or land degradation.

  5. Alert System: Generate real-time alerts for identified anomalies, notifying ground teams for further analysis and response.

  6. Data Archival & Reporting: Store identified anomalies in a central database and generate comprehensive reports for ongoing monitoring and record-keeping.

Implementation (Python): Anomaly Detection in Satellite Imagery

This Python code uses a pre-trained CNN model to detect anomalies in satellite images in near real-time.

import cv2
import numpy as np
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt

# Load pre-trained CNN model for anomaly detection
model = load_model('satellite_anomaly_detection_model.h5')

def preprocess_image(image_path):
    # Load and normalize satellite image
    img = cv2.imread(image_path)
    img_resized = cv2.resize(img, (256, 256))  # resize for model input
    img_normalized = img_resized / 255.0
    return img_normalized

def detect_anomaly(image):
    # Predict anomaly presence
    reshaped_image = np.expand_dims(image, axis=0)
    prediction = model.predict(reshaped_image)
    return prediction

def visualize_anomaly(image, prediction):
    plt.figure(figsize=(10, 5))
    plt.subplot(1, 2, 1)
    plt.imshow(image)
    plt.title("Satellite Image")
    plt.subplot(1, 2, 2)
    plt.imshow(prediction[0, :, :, 0], cmap='hot')
    plt.title("Anomaly Detection")
    plt.show()

# Example usage
image = preprocess_image('example_satellite_image.png')
anomaly_prediction = detect_anomaly(image)
visualize_anomaly(image, anomaly_prediction)

Final Output

This pipeline and code facilitate real-time monitoring for aerospace applications, enabling rapid detection and response to environmental anomalies.

References

  • todo