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Updated: May 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A deep inverse convolutional neural network-based semantic classification method for land cover remote sensing

Ming Wang¹, Anqi She², Hao Chang³

¹Network Information Center, Jilin Normal University, Siping, 136000, China.

Scientific Reports

|March 28, 2024

Summary

A novel deep deconvolutional neural network method accurately classifies imbalanced land cover remote sensing images. This approach improves recognition of minority categories, enhancing overall semantic classification accuracy.

Keywords:

Deep inverse convolutional neural network Feature extraction Land cover Remote sensing images Semantic classification Semantic segmentation

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Area of Science:

Remote Sensing
Computer Vision
Machine Learning

Background:

Land cover classification in remote sensing is challenged by imbalanced data, where some categories are rare, leading to poor recognition of minority classes.
Existing methods struggle with multi-target semantic classification of land cover remote sensing images due to data imbalance.

Purpose of the Study:

To propose a semantic classification method for land cover remote sensing images that addresses data imbalance.
To enhance the accuracy of multi-target semantic classification for land cover remote sensing images.

Main Methods:

A deep deconvolutional neural network was employed for semantic segmentation of land cover remote sensing images.
An improved sequential clustering algorithm extracted four semantic features: color, texture, shape, and size.
A random forest algorithm was utilized for classifying and recognizing these extracted semantic features.

Main Results:

The proposed method achieved high accuracy in classifying multi-target semantic types of land cover remote sensing images.
Average Dice similarity coefficient reached 0.9877, indicating precise segmentation.
Average Hausdorff distance was 0.9911, confirming accurate boundary delineation.

Conclusions:

The developed deep deconvolutional neural network method effectively overcomes the challenge of imbalanced land cover data.
This approach significantly improves the semantic classification accuracy of land cover remote sensing images, especially for minority categories.