scholarly journals Refined UNet V2: End-to-End Patch-Wise Network for Noise-Free Cloud and Shadow Segmentation

2020 ◽  
Vol 12 (21) ◽  
pp. 3530
Author(s):  
Libin Jiao ◽  
Lianzhi Huo ◽  
Changmiao Hu ◽  
Ping Tang
Keyword(s):  
Message Passing ◽  
Conditional Random Field ◽  
High Dimensional ◽  
Gaussian Filter ◽  
Brute Force ◽  
Time Consumption ◽  
End To End ◽  
Joint Prediction ◽  
Essential Prerequisite ◽  
Potential Efficiency

Cloud and shadow detection is an essential prerequisite for further remote sensing processing, whereas edge-precise segmentation remains a challenging issue. In Refined UNet, we considered the aforementioned task and proposed a two-stage pipeline to achieve the edge-precise segmentation. The isolated segmentation regions in Refined UNet, however, bring inferior visualization and should be sufficiently eliminated. Moreover, an end-to-end model is also expected to jointly predict and refine the segmentation results. In this paper, we propose the end-to-end Refined UNet v2 to achieve joint prediction and refinement of cloud and shadow segmentation, which is capable of visually neutralizing redundant segmentation pixels or regions. To this end, we inherit the pipeline of Refine UNet, revisit the bilateral message passing in the inference of conditional random field (CRF), and then develop a novel bilateral strategy derived from the Guided Gaussian filter. Derived from a local linear model of denoising, our v2 can considerably remove isolated segmentation pixels or regions, which is able to yield “cleaner” results. Compared to the high-dimensional Gaussian filter, the Guided Gaussian filter-based message-passing strategy is quite straightforward and easy to implement so that a brute-force implementation can be easily given in GPU frameworks, which is potentially efficient and facilitates embedding. Moreover, we prove that Guided Gaussian filter-based message passing is highly relevant to the Gaussian bilateral term in Dense CRF. Experiments and results demonstrate that our v2 is quantitatively comparable to Refined UNet, but can visually outperform that from the noise-free segmentation perspective. The comparison of time consumption also supports the potential efficiency of our v2.

scholarly journals Refined UNet V4: End-to-End Patch-Wise Network for Cloud and Shadow Segmentation with Bilateral Grid

2022 ◽  
Vol 14 (2) ◽  
pp. 358
Author(s):  
Libin Jiao ◽  
Lianzhi Huo ◽  
Changmiao Hu ◽  
Ping Tang ◽  
Zheng Zhang
Keyword(s):  
Remote Sensing ◽  
Message Passing ◽  
Shadow Detection ◽  
Landsat 8 ◽  
Gaussian Filter ◽  
Computationally Efficient ◽  
Remote Sensing Images ◽  
Gaussian Form ◽  
End To End ◽  
High Level

Remote sensing images are usually contaminated by cloud and corresponding shadow regions, making cloud and shadow detection one of the essential prerequisites for processing and translation of remote sensing images. Edge-precise cloud and shadow segmentation remains challenging due to the inherent high-level semantic acquisition of current neural segmentation fashions. We, therefore, introduce the Refined UNet series to partially achieve edge-precise cloud and shadow detection, including two-stage Refined UNet, v2 with a potentially efficient gray-scale guided Gaussian filter-based CRF, and v3 with an efficient multi-channel guided Gaussian filter-based CRF. However, it is visually demonstrated that the locally linear kernel used in v2 and v3 is not sufficiently sensitive to potential edges in comparison with Refined UNet. Accordingly, we turn back to the investigation of an end-to-end UNet-CRF architecture with a Gaussian-form bilateral kernel and its relatively efficient approximation. In this paper, we present Refined UNet v4, an end-to-end edge-precise segmentation network for cloud and shadow detection, which is capable of retrieving regions of interest with relatively tight edges and potential shadow regions with ambiguous edges. Specifically, we inherit the UNet-CRF architecture exploited in the Refined UNet series, which concatenates a UNet backbone of coarsely locating cloud and shadow regions and an embedded CRF layer of refining edges. In particular, the bilateral grid-based approximation to the Gaussian-form bilateral kernel is applied to the bilateral message-passing step, in order to ensure the delineation of sufficiently tight edges and the retrieval of shadow regions with ambiguous edges. Our TensorFlow implementation of the bilateral approximation is relatively computationally efficient in comparison with Refined UNet, attributed to the straightforward GPU acceleration. Extensive experiments on Landsat 8 OLI dataset illustrate that our v4 can achieve edge-precise cloud and shadow segmentation and improve the retrieval of shadow regions, and also confirm its computational efficiency.

scholarly journals Optimal errors and phase transitions in high-dimensional generalized linear models

2019 ◽  
Vol 116 (12) ◽  
pp. 5451-5460 ◽  
Author(s):  
Jean Barbier ◽  
Florent Krzakala ◽  
Nicolas Macris ◽  
Léo Miolane ◽  
Lenka Zdeborová
Keyword(s):  
Phase Transitions ◽  
Generalized Linear Models ◽  
Message Passing ◽  
Statistical Physics ◽  
Linear Models ◽  
Optimal Estimation ◽  
Error Correcting Codes ◽  
Data Matrix ◽  
High Dimensional ◽  
Special Cases

Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-correcting codes, or benchmark models in neural networks. We evaluate the mutual information (or “free entropy”) from which we deduce the Bayes-optimal estimation and generalization errors. Our analysis applies to the high-dimensional limit where both the number of samples and the dimension are large and their ratio is fixed. Nonrigorous predictions for the optimal errors existed for special cases of GLMs, e.g., for the perceptron, in the field of statistical physics based on the so-called replica method. Our present paper rigorously establishes those decades-old conjectures and brings forward their algorithmic interpretation in terms of performance of the generalized approximate message-passing algorithm. Furthermore, we tightly characterize, for many learning problems, regions of parameters for which this algorithm achieves the optimal performance and locate the associated sharp phase transitions separating learnable and nonlearnable regions. We believe that this random version of GLMs can serve as a challenging benchmark for multipurpose algorithms.

scholarly journals Guided Networks for Few-Shot Image Segmentation and Fully Connected CRFs

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1508
Author(s):  
Kun Zhang ◽  
Yuanjie Zheng ◽  
Xiaobo Deng ◽  
Weikuan Jia ◽  
Jian Lian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Fusion ◽  
Object Segmentation ◽  
Conditional Random Field ◽  
High Dimensional ◽  
Learning Method ◽  
Deep Convolutional Neural Networks ◽  
Structured Output ◽  
Fully Connected ◽  
Task Representations

The goal of the few-shot learning method is to learn quickly from a low-data regime. Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent. For this problem, we propose improved guided networks and combine them with a fully connected conditional random field (CRF). The guided network extracts task representations from annotated support images through feature fusion to do fast, accurate inference on new unannotated query images. By bringing together few-shot learning methods and fully connected CRFs, our method can do accurate object segmentation by overcoming poor localization properties of deep convolutional neural networks and can quickly updating tasks, without further optimization, when faced with new data. Our guided network is at the forefront of accuracy for the terms of annotation volume and time.

scholarly journals Review of Stereo Matching Algorithms Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Kun Zhou ◽  
Xiangxi Meng ◽  
Bo Cheng
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Learning Algorithms ◽  
Time Consumption ◽  
End To End ◽  
Traditional Approaches ◽  
Speed Accuracy ◽  
Comprehensive Coverage

Stereo vision is a flourishing field, attracting the attention of many researchers. Recently, leveraging on the development of deep learning, stereo matching algorithms have achieved remarkable performance far exceeding traditional approaches. This review presents an overview of different stereo matching algorithms based on deep learning. For convenience, we classified the algorithms into three categories: (1) non-end-to-end learning algorithms, (2) end-to-end learning algorithms, and (3) unsupervised learning algorithms. We have provided a comprehensive coverage of the remarkable approaches in each category and summarized the strengths, weaknesses, and major challenges, respectively. The speed, accuracy, and time consumption were adopted to compare the different algorithms.

scholarly journals State evolution for approximate message passing with non-separable functions

2019 ◽  
Vol 9 (1) ◽  
pp. 33-79 ◽  
Author(s):  
Raphaël Berthier ◽  
Andrea Montanari ◽  
Phan-Minh Nguyen
Keyword(s):  
Message Passing ◽  
Phase Retrieval ◽  
Robust Regression ◽  
Empirical Work ◽  
Data Matrix ◽  
High Dimensional ◽  
Regularity Conditions ◽  
Approximate Message Passing ◽  
Separable Functions ◽  
State Evolution

Abstract Given a high-dimensional data matrix $\boldsymbol{A}\in{{\mathbb{R}}}^{m\times n}$, approximate message passing (AMP) algorithms construct sequences of vectors $\boldsymbol{u}^{t}\in{{\mathbb{R}}}^{n}$, ${\boldsymbol v}^{t}\in{{\mathbb{R}}}^{m}$, indexed by $t\in \{0,1,2\dots \}$ by iteratively applying $\boldsymbol{A}$ or $\boldsymbol{A}^{{\textsf T}}$ and suitable nonlinear functions, which depend on the specific application. Special instances of this approach have been developed—among other applications—for compressed sensing reconstruction, robust regression, Bayesian estimation, low-rank matrix recovery, phase retrieval and community detection in graphs. For certain classes of random matrices $\boldsymbol{A}$, AMP admits an asymptotically exact description in the high-dimensional limit $m,n\to \infty $, which goes under the name of state evolution. Earlier work established state evolution for separable nonlinearities (under certain regularity conditions). Nevertheless, empirical work demonstrated several important applications that require non-separable functions. In this paper we generalize state evolution to Lipschitz continuous non-separable nonlinearities, for Gaussian matrices $\boldsymbol{A}$. Our proof makes use of Bolthausen’s conditioning technique along with several approximation arguments. In particular, we introduce a modified algorithm (called LoAMP for Long AMP), which is of independent interest.

Sign in / Sign up

Export Citation Format

Share Document