390 research outputs found
SIMCO: SIMilarity-based object COunting
We present SIMCO, the first agnostic multi-class object counting approach.
SIMCO starts by detecting foreground objects through a novel Mask RCNN-based
architecture trained beforehand (just once) on a brand-new synthetic 2D shape
dataset, InShape; the idea is to highlight every object resembling a primitive
2D shape (circle, square, rectangle, etc.). Each object detected is described
by a low-dimensional embedding, obtained from a novel similarity-based head
branch; this latter implements a triplet loss, encouraging similar objects
(same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this
embedding for clustering, so that different types of objects can emerge and be
counted, making SIMCO the very first multi-class unsupervised counter.
Experiments show that SIMCO provides state-of-the-art scores on counting
benchmarks and that it can also help in many challenging image understanding
tasks
Rich probabilistic models for semantic labeling
Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung
Deep learning-based change detection in remote sensing images:a review
Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods
Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
We address the problem of scene classification from optical remote sensing
(RS) images based on the paradigm of hierarchical metric learning. Ideally,
supervised metric learning strategies learn a projection from a set of training
data points so as to minimize intra-class variance while maximizing inter-class
separability to the class label space. However, standard metric learning
techniques do not incorporate the class interaction information in learning the
transformation matrix, which is often considered to be a bottleneck while
dealing with fine-grained visual categories. As a remedy, we propose to
organize the classes in a hierarchical fashion by exploring their visual
similarities and subsequently learn separate distance metric transformations
for the classes present at the non-leaf nodes of the tree. We employ an
iterative max-margin clustering strategy to obtain the hierarchical
organization of the classes. Experiment results obtained on the large-scale
NWPU-RESISC45 and the popular UC-Merced datasets demonstrate the efficacy of
the proposed hierarchical metric learning based RS scene recognition strategy
in comparison to the standard approaches.Comment: Undergoing revision in GRS
Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose two new network architectures trained with loss functions weighted by priors that reduce the impact of change pixels on the learning objective. The change prior is derived in an unsupervised fashion from relational pixel information captured by domain-specific affinity matrices. Specifically, we use the vertex degrees associated with an absolute affinity difference matrix and demonstrate their utility in combination with cycle consistency and adversarial training. The proposed neural networks are compared with the state-of-the-art algorithms. Experiments conducted on three real data sets show the effectiveness of our methodology
Assessment of Different Methods for Shadow Detection in High-Resolution Optical Imagery and Evaluation of Shadow Impact on Calculation of NDVI and Evapotranspiration
Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the high-resolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level
Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration
Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the high-resolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level.info:eu-repo/semantics/acceptedVersio
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