Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments

• URL: http://arxiv.org/abs/2106.07113v1
• Date: Sun, 13 Jun 2021 23:50:05 GMT
• Title: Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments
• Authors: Sarah Chen, Esther Cao, Anirudh Koul, Siddha Ganju, Satyarth Praveen, Meher Anand Kasam
• Abstract summary: NASA Terra and NASA Aqua satellites capture imagery containing swath gaps, which are areas of no data. With annotated data as supervision, a model can learn to differentiate between the area of focus and the swath gap. We propose an augmentation technique that considerably removes the existence of swath gaps in order to allow CNNs to focus on the region of interest.
• Score: 0.6157382820537718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the nature of their pathways, NASA Terra and NASA Aqua satellites capture imagery containing swath gaps, which are areas of no data. Swath gaps can overlap the region of interest (ROI) completely, often rendering the entire imagery unusable by Machine Learning (ML) models. This problem is further exacerbated when the ROI rarely occurs (e.g. a hurricane) and, on occurrence, is partially overlapped with a swath gap. With annotated data as supervision, a model can learn to differentiate between the area of focus and the swath gap. However, annotation is expensive and currently the vast majority of existing data is unannotated. Hence, we propose an augmentation technique that considerably removes the existence of swath gaps in order to allow CNNs to focus on the ROI, and thus successfully use data with swath gaps for training. We experiment on the UC Merced Land Use Dataset, where we add swath gaps through empty polygons (up to 20 percent areas) and then apply augmentation techniques to fill the swath gaps. We compare the model trained with our augmentation techniques on the swath gap-filled data with the model trained on the original swath gap-less data and note highly augmented performance. Additionally, we perform a qualitative analysis using activation maps that visualizes the effectiveness of our trained network in not paying attention to the swath gaps. We also evaluate our results with a human baseline and show that, in certain cases, the filled swath gaps look so realistic that even a human evaluator did not distinguish between original satellite images and swath gap-filled images. Since this method is aimed at unlabeled data, it is widely generalizable and impactful for large scale unannotated datasets from various space data domains.

Related papers

• Getting it Right: Improving Spatial Consistency in Text-to-Image Models [103.52640413616436]
  One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. We create SPRIGHT, the first spatially-focused, large scale dataset, by re-captioning 6 million images from 4 widely used vision datasets. We attain state-of-the-art on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on 500 images.
  arXiv  Detail & Related papers  (2024-04-01T15:55:25Z)
• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• Knowledge Combination to Learn Rotated Detection Without Rotated Annotation [53.439096583978504]
  Rotated bounding boxes drastically reduce output ambiguity of elongated objects. Despite the effectiveness, rotated detectors are not widely employed. We propose a framework that allows the model to predict precise rotated boxes.
  arXiv  Detail & Related papers  (2023-04-05T03:07:36Z)
• Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network [102.21368201494909]
  Model inversion (MI) attacks have raised increasing concerns about privacy. Recent MI attacks leverage a generative adversarial network (GAN) as an image prior to narrow the search space. We propose Pseudo Label-Guided MI (PLG-MI) attack via conditional GAN (cGAN)
  arXiv  Detail & Related papers  (2023-02-20T07:29:34Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery [0.0]
  Self-supervised learning has been applied in the remote sensing domain to exploit readily-available unlabeled data. In this paper, we study self-supervised visual representation learning through the lens of label efficiency.
  arXiv  Detail & Related papers  (2022-10-13T06:54:13Z)
• Region-aware Attention for Image Inpainting [33.22497212024083]
  We propose a novel region-aware attention (RA) module for inpainting images. By avoiding the directly calculating corralation between each pixel pair in a single samples, the misleading of invalid information in holes can be avoided. A learnable region dictionary (LRD) is introduced to store important information in the entire dataset. Our methodscan generate semantically plausible results with realistic details.
  arXiv  Detail & Related papers  (2022-04-03T06:26:22Z)
• Smoothing the Generative Latent Space with Mixup-based Distance Learning [32.838539968751924]
  We consider the situation where neither large scale dataset of our interest nor transferable source dataset is available. We propose latent mixup-based distance regularization on the feature space of both a generator and the counterpart discriminator.
  arXiv  Detail & Related papers  (2021-11-23T06:39:50Z)
• Improving Deep Metric Learning by Divide and Conquer [11.380358587116683]
  Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. We propose to build a more expressive representation by splitting the embedding space and the data hierarchically into smaller sub-parts.
  arXiv  Detail & Related papers  (2021-09-09T02:57:34Z)
• Analyzing the Dependency of ConvNets on Spatial Information [81.93266969255711]
  We propose spatial shuffling and GAP+FC to destroy spatial information during both training and testing phases. We observe that spatial information can be deleted from later layers with small performance drops.
  arXiv  Detail & Related papers  (2020-02-05T15:22:32Z)

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.