Классификация методов сегментации снимков земной поверхности

В данной работе представлена классификация методов сегментации снимков земной поверхности. Рассмотрены такие подходы как сравнение с шаблоном, машинное обучение и глубокие нейронные сети, а также применение знаний об анализируемых объектах. Рассмотрены особенности применения вегетационных индексов для сегментации данных по спутниковым снимкам. Отмечены преимущества и недостатки. Систематизированы результаты, полученные авторами методик, появившихся за последние 10 лет, что позволит заинтересованным быстрее сориентироваться, сформировать идеи для последующих исследований

Snitching on ditches: tracking salt marsh health using transfer learning

31 pagesCoastal salt marshes offer crucial ecological benefits, including carbon sequestration, habitat for many species, and protection against storm surges and erosion. However, human activity has led to significant dieback of these ecosystems on both a national and global scale. Much of the northeastern US salt marshes are experiencing exacerbated loss due to grid ditching, an outdated practice in which standing pools of water were drained by a series of narrow ditches to reduce mosquito populations. Identifying ditches is an important step in tracking salt marsh health, yet ecologists currently lack an efficient method to do so, mostly relying on walking the fields between tides or manually delineating ditches in aerial imagery. This project investigates an alternate workflow for identifying ditches in high-resolution drone imagery captured by the Salt Marsh UAV group at University of Massachusetts Amherst. I implement U-Net, a machine learning that originates from medical imaging, to sift through all the varied water features in a single salt marsh site and classify each pixel in an image as background, ditch, or non-ditch, a process called semantic segmentation. Ultimately, the goal is to produce georeferenced shapefiles that precisely locate ditches on the ground. I use pre-trained versions of U-net and experiment with various parameters to tune the models for optimal results. This is a form of transfer learning, taking models from one domain and repurposing them for another. MobileNet-UNet exhibits the highest performance and produces strong ditch segmentation results that ecologists can utilize with minimal post-processing. Future research should experiment with using multispectral bands like near-infrared (NIR) and short-wave infrared (SWIR) or a Digital Elevation Model (DEM) to provide the model with more information. This project provides ecologists with an automated method of identifying ditches and demonstrates that transfer learning is a viable alternative to traditional remote sensing water feature extraction methods

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet