6 research outputs found
Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset
Vehicle classification is a hot computer vision topic, with studies ranging
from ground-view up to top-view imagery. In remote sensing, the usage of
top-view images allows for understanding city patterns, vehicle concentration,
traffic management, and others. However, there are some difficulties when
aiming for pixel-wise classification: (a) most vehicle classification studies
use object detection methods, and most publicly available datasets are designed
for this task, (b) creating instance segmentation datasets is laborious, and
(c) traditional instance segmentation methods underperform on this task since
the objects are small. Thus, the present research objectives are: (1) propose a
novel semi-supervised iterative learning approach using GIS software, (2)
propose a box-free instance segmentation approach, and (3) provide a city-scale
vehicle dataset. The iterative learning procedure considered: (1) label a small
number of vehicles, (2) train on those samples, (3) use the model to classify
the entire image, (4) convert the image prediction into a polygon shapefile,
(5) correct some areas with errors and include them in the training data, and
(6) repeat until results are satisfactory. To separate instances, we considered
vehicle interior and vehicle borders, and the DL model was the U-net with the
Efficient-net-B7 backbone. When removing the borders, the vehicle interior
becomes isolated, allowing for unique object identification. To recover the
deleted 1-pixel borders, we proposed a simple method to expand each prediction.
The results show better pixel-wise metrics when compared to the Mask-RCNN (82%
against 67% in IoU). On per-object analysis, the overall accuracy, precision,
and recall were greater than 90%. This pipeline applies to any remote sensing
target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa
Panoptic Segmentation Meets Remote Sensing
Panoptic segmentation combines instance and semantic predictions, allowing
the detection of "things" and "stuff" simultaneously. Effectively approaching
panoptic segmentation in remotely sensed data can be auspicious in many
challenging problems since it allows continuous mapping and specific target
counting. Several difficulties have prevented the growth of this task in remote
sensing: (a) most algorithms are designed for traditional images, (b) image
labelling must encompass "things" and "stuff" classes, and (c) the annotation
format is complex. Thus, aiming to solve and increase the operability of
panoptic segmentation in remote sensing, this study has five objectives: (1)
create a novel data preparation pipeline for panoptic segmentation, (2) propose
an annotation conversion software to generate panoptic annotations; (3) propose
a novel dataset on urban areas, (4) modify the Detectron2 for the task, and (5)
evaluate difficulties of this task in the urban setting. We used an aerial
image with a 0,24-meter spatial resolution considering 14 classes. Our pipeline
considers three image inputs, and the proposed software uses point shapefiles
for creating samples in the COCO format. Our study generated 3,400 samples with
512x512 pixel dimensions. We used the Panoptic-FPN with two backbones
(ResNet-50 and ResNet-101), and the model evaluation considered semantic
instance and panoptic metrics. We obtained 93.9, 47.7, and 64.9 for the mean
IoU, box AP, and PQ. Our study presents the first effective pipeline for
panoptic segmentation and an extensive database for other researchers to use
and deal with other data or related problems requiring a thorough scene
understanding.Comment: 40 pages, 10 figures, submitted to journa
Utilização de imagens Landsat 8 (OLI) para mapeamento de áreas cultiváveis com arroz no estado de Santa Catarina – Brasil, safra 2014/2015 e 2015/2016
O objetivo principal deste trabalho foi mapear e dar consistência às estimativas e previsões de produção de arroz no Estado de Santa Catarina e acompanhar a expansão ou retração da cultura, por meio das imagens Lansdat 8. A metodologia consistiu nos seguintes procedimentos: seleção de cenas de imagem Landsat 8 (OLI), processamento digital, segmentação, interpretação visual, trabalho de campo e validação dos dados. Foram obtidos dois mapas de ocupação de arroz irrigado, um para o ano safra 2014/2015 e outro para 2015/2016. A validação do mapeamento com o índice Kappa mostrou um alto nível de concordância dos dados. Este mapeamento detectou uma redução no plantio da cultura em relação ao ano anterior, provando ser eficiente porque acompanhou a retração da cultura. A análise temporal do Landsat 8 (OLI) foi essencial para diferenciar a dinâmica das culturas e para interpretar o cultivo do arroz
Bounding box-free instance segmentation using semi-supervised iterative learning for vehicle detection
Vehicle classification is a hot computer vision topic, with studies ranging from ground-view to top-view imagery.
Top-view images allow understanding city patterns, traffic management, among others. However, there are some difficulties for
pixel-wise classification: most vehicle classification studies use object detection methods, and most publicly available datasets
are designed for this task, creating instance segmentation datasets is laborious, and traditional instance segmentation methods
underperform on this task since the objects are small. Thus, the present research objectives are as follows: first, propose a novel
semisupervised iterative learning approach using the geographic information system software, second, propose a box-free instance segmentation approach, and third, provide a city-scale vehicle dataset. The iterative learning procedure considered the following: first, labeling a few vehicles from the entire scene, second, choosing training samples near those areas, third, training the deep learning model (U-net with efficient-net-B7 backbone), fourth, classifying the whole scene, fifth, converting the predictions into shapefile, sixth, correcting areas with wrong predictions, seventh, including them in the training data, eighth repeating until results are satisfactory. We considered vehicle interior and borders to separate instances using a semantic segmentation model. When removing the borders, the vehicle interior becomes isolated, allowing for unique object identification. Our procedure is very efficient and accurate for generating data iteratively, which resulted in 122 567 mapped vehicles. Metrics-wise, our method presented higher intersection over union when compared to box-based methods (82% against 72%), and per-object metrics surpassed 90% for precision and recall