6,199 research outputs found
Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image
Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Color image segmentation using saturated RGB colors and decoupling the intensity from the hue
Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
Computer vision is experiencing an AI renaissance, in which machine learning
models are expediting important breakthroughs in academic research and
commercial applications. Effectively training these models, however, is not
trivial due in part to hyperparameters: user-configured values that control a
model's ability to learn from data. Existing hyperparameter optimization
methods are highly parallel but make no effort to balance the search across
heterogeneous hardware or to prioritize searching high-impact spaces. In this
paper, we introduce a framework for massively Scalable Hardware-Aware
Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the
relative complexity of each search space and monitors performance on the
learning task over all trials. These metrics are then used as heuristics to
assign hyperparameters to distributed workers based on their hardware. We first
demonstrate that our framework achieves double the throughput of a standard
distributed hyperparameter optimization framework by optimizing SVM for MNIST
using 150 distributed workers. We then conduct model search with SHADHO over
the course of one week using 74 GPUs across two compute clusters to optimize
U-Net for a cell segmentation task, discovering 515 models that achieve a lower
validation loss than standard U-Net.Comment: 10 pages, 6 figure
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