Exploring Self-Attention for Crop-type Classification Explainability

• URL: http://arxiv.org/abs/2210.13167v1
• Date: Mon, 24 Oct 2022 12:36:40 GMT
• Title: Exploring Self-Attention for Crop-type Classification Explainability
• Authors: Ivica Obadic, Ribana Roscher, Dario Augusto Borges Oliveira and Xiao Xiang Zhu
• Abstract summary: We introduce a novel explainability framework that aims to shed a light on the essential crop disambiguation patterns learned by a state-of-the-art transformer encoder model. We also present a sensitivity analysis approach to understand better the attention capability for revealing crop-specific phenological events.
• Score: 15.822486263693355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated crop-type classification using Sentinel-2 satellite time series is essential to support agriculture monitoring. Recently, deep learning models based on transformer encoders became a promising approach for crop-type classification. Using explainable machine learning to reveal the inner workings of these models is an important step towards improving stakeholders' trust and efficient agriculture monitoring. In this paper, we introduce a novel explainability framework that aims to shed a light on the essential crop disambiguation patterns learned by a state-of-the-art transformer encoder model. More specifically, we process the attention weights of a trained transformer encoder to reveal the critical dates for crop disambiguation and use domain knowledge to uncover the phenological events that support the model performance. We also present a sensitivity analysis approach to understand better the attention capability for revealing crop-specific phenological events. We report compelling results showing that attention patterns strongly relate to key dates, and consequently, to the critical phenological events for crop-type classification. These findings might be relevant for improving stakeholder trust and optimizing agriculture monitoring processes. Additionally, our sensitivity analysis demonstrates the limitation of attention weights for identifying the important events in the crop phenology as we empirically show that the unveiled phenological events depend on the other crops in the data considered during training.

Related papers

• Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis [0.0]
  Time series remote sensing data were used for the generation of the cropping pattern. Classification algorithms are used to classify crop patterns and mapped agriculture land used. In this paper, we are proposing Deep Neural Network (DNN) based classification to improve the performance of crop pattern recognition.
  arXiv  Detail & Related papers  (2024-11-19T17:19:20Z)
• Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence [92.07601770031236]
  We investigate semantically meaningful patterns in the attention heads of an encoder-only Transformer architecture. We find that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization.
  arXiv  Detail & Related papers  (2024-09-20T07:41:47Z)
• Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing [6.65506917941232]
  We focus on the task of crop yield prediction, specifically for soybean, wheat, and rapeseed crops in Argentina, Uruguay, and Germany. Our goal is to develop and explain predictive models for these crops, using a large dataset of satellite images, additional data modalities, and crop yield maps. For model explainability, we utilize feature attribution methods to quantify input feature contributions, identify critical growth stages, analyze yield variability at the field level, and explain less accurate predictions.
  arXiv  Detail & Related papers  (2024-07-11T08:23:46Z)
• The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models [4.089889918897877]
  This study challenges the prevailing assumption that vision-based gait recognition relies primarily on motion patterns. We show through a comparative analysis that removing height information leads to notable performance degradation. We propose a spatial transformer model processing individual poses, disregarding any temporal information, which achieves unreasonably good accuracy.
  arXiv  Detail & Related papers  (2024-02-13T09:33:12Z)
• Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability [0.6131022957085438]
  Grasslands are known for their high biodiversity and ability to provide multiple ecosystem services. Challenges in automating the identification of indicator plants are key obstacles to large-scale grassland monitoring. This paper delves into the latter two challenges, with a specific focus on transfer learning and XAI approaches to grassland monitoring.
  arXiv  Detail & Related papers  (2023-12-13T10:17:48Z)
• Food Image Classification and Segmentation with Attention-based Multiple Instance Learning [51.279800092581844]
  The paper presents a weakly supervised methodology for training food image classification and semantic segmentation models. The proposed methodology is based on a multiple instance learning approach in combination with an attention-based mechanism. We conduct experiments on two meta-classes within the FoodSeg103 data set to verify the feasibility of the proposed approach.
  arXiv  Detail & Related papers  (2023-08-22T13:59:47Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers [79.60022233109397]
  We present spatial prior attention (SPAN), a framework that takes advantage of consistent spatial and semantic structure in unlabeled image datasets. SPAN operates by regularizing attention masks from separate transformer heads to follow various priors over semantic regions. We find that the resulting attention masks are more interpretable than those derived from domain-agnostic pretraining.
  arXiv  Detail & Related papers  (2022-09-07T02:30:36Z)
• How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction [66.09605613944201]
  We quantitatively evaluate the effect of attention and Knowledge Graph on bag-level relation extraction (RE) We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns; and (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior.
  arXiv  Detail & Related papers  (2021-07-26T09:38:28Z)
• Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention [9.427845067849177]
  We explore the idea of using object detection as a form of attention to mitigate the effects of data variability. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification.
  arXiv  Detail & Related papers  (2021-06-03T21:22:18Z)
• SparseBERT: Rethinking the Importance Analysis in Self-attention [107.68072039537311]
  Transformer-based models are popular for natural language processing (NLP) tasks due to its powerful capacity. Attention map visualization of a pre-trained model is one direct method for understanding self-attention mechanism. We propose a Differentiable Attention Mask (DAM) algorithm, which can be also applied in guidance of SparseBERT design.
  arXiv  Detail & Related papers  (2021-02-25T14:13:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.