High-Resolution Semantic Labeling with Convolutional Neural Networks (1611.01962v1)

Abstract: Convolutional neural networks (CNNs) have received increasing attention over the last few years. They were initially conceived for image categorization, i.e., the problem of assigning a semantic label to an entire input image. In this paper we address the problem of dense semantic labeling, which consists in assigning a semantic label to every pixel in an image. Since this requires a high spatial accuracy to determine where labels are assigned, categorization CNNs, intended to be highly robust to local deformations, are not directly applicable. By adapting categorization networks, many semantic labeling CNNs have been recently proposed. Our first contribution is an in-depth analysis of these architectures. We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties. We observe that even though they provide competitive results, these CNNs often underexploit properties of semantic labeling that could lead to more effective and efficient architectures. Out of these observations, we then derive a CNN framework specifically adapted to the semantic labeling problem. In addition to learning features at different resolutions, it learns how to combine these features. By integrating local and global information in an efficient and flexible manner, it outperforms previous techniques. We evaluate the proposed framework and compare it with state-of-the-art architectures on public benchmarks of high-resolution aerial image labeling.

• The paper adapts convolutional neural networks for high-resolution pixel-level semantic labeling, addressing the challenge of achieving high spatial accuracy efficiently.
• It evaluates existing semantic labeling network architectures, including dilation, deconvolution, and skip networks, highlighting their limitations.
• A novel MLP network leveraging multi-layer perceptrons over concatenated features is introduced, demonstrating superior accuracy and efficiency on standard datasets.

High-Resolution Semantic Labeling with Convolutional Neural Networks

In recent developments within image processing research, the paper "High-Resolution Semantic Labeling with Convolutional Neural Networks" explores the adaptation of convolutional neural networks (CNNs) from generalized image categorization to specific dense semantic labeling tasks, focusing on assigning semantic labels to individual pixels in high-resolution aerial images. The underlying challenge addressed is achieving high spatial accuracy while maintaining computational efficiency in CNN architectures.

Architectural Analysis and Proposed Solutions

1. Adapting Conventional CNNs: The paper begins by acknowledging that traditional CNNs are designed for image categorization, where entire images are labeled as single semantic entities, often sacrificing spatial precision for robustness against local deformations. This makes them inherently unsuitable for tasks requiring precise pixel-level annotations.
2. Evaluation of Existing Networks: The authors evaluate current architectures engineered for semantic labeling, categorizing them into dilation networks, deconvolution networks with unpooling, and skip networks. Each approach leverages different methodologies to balance recognition precision and spatial resolution.

• Dilation Networks: Utilizing dilated convolutions, these architectures interleave multiple low-resolution outputs, providing increased receptive fields without increasing network size. However, they remain computationally intensive.
• Deconvolution Networks: These models aim to reconstruct high-resolution outputs by reflecting each layer of an FCN. While they enhance spatial detailing, they are susceptible to artifacts and computational burdens due to increased network depth.
• Skip Networks: By integrating intermediate feature maps from different layers, skip networks seek to combine resolution-specific tasks efficiently. Though beneficial, they often lack flexibility in feature combination.

1. Introduction of MLP Networks: The paper presents a novel architecture, utilizing multi-layer perceptrons (MLPs) over concatenated features extracted at different resolutions from a single CNN. This methodology enriches the ability to integrate both local and global image features more effectively than existing architectures by allowing non-linear feature combinations.

Numerical Results and Comparative Analysis

The proposed MLP network demonstrates superior performance in both accuracy and computational efficiency, as evidenced by experiments on the standardized Vaihingen and Potsdam datasets. Compared to base FCNs and derived architectures such as skip and unpooling networks, the MLP network consistently delivers higher mean F1-scores and overall accuracy, affirming its strength in high-resolution semantic labeling.

Further comparison with other state-of-the-art methods, including dilation networks and ensemble frameworks integrating CNNs with random forests and CRFs, positions MLP as a leading approach in both accuracy and processing speed. Additionally, the submission of MLP-generated results to the ISPRS Semantic Labeling Contest garnered high ranks, underscoring its practical applicability.

Implications and Future Directions

The architecture provisions established in this research highlight the importance of resolution-aware feature combination in semantic labeling CNNs. The simplification and efficiency of the MLP approach suggest potential broader applicability beyond aerial imagery, potentially influencing advancements in autonomous navigation and urban planning.

The future trajectory proposed by the authors points towards expanding dataset diversity and scale, which could further enhance architecture robustness and generalizability. Moreover, continuous integration of theoretical insights into neural networks would facilitate the evolution of semantic labeling frameworks, maximizing both effectiveness and adaptability in processing ever-increasing volumes of complex imagery.

This essay underscores the paper's contribution to refining CNN architectures for pixel-wise semantic annotation. The proposed MLP model not only offers a robust solution but also directs future endeavors towards integrating theoretical machine learning frameworks with practical, large-scale image understanding challenges.