24 research outputs found
Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images
Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.Comment: This manuscript is the accepted version for Proceedings of the IEE
Effective SAR sea ice image segmentation and touch floe separation using a combined multi-stage approach
Accurate sea-ice segmentation from satellite synthetic aperture radar (SAR) images plays an important role for understanding the interactions between sea-ice, ocean and atmosphere in the Arctic. Processing sea-ice SAR images are challenging due to poor spatial resolution and severe speckle noise. In this paper, we present a multi-stage method for the sea-ice SAR image segmentation, which includes edge-preserved filtering for pre-processing, k-means clustering for segmentation and conditional morphology filtering for post-processing. As such, the effect of noise has been suppressed and the under-segmented regions are successfully corrected
Analyzing Depthwise Convolution Based Neural Network: Study Case in Ship Detection and Land Cover Classification
Various methods are available to perform feature extraction on satellite images. Among the available alternatives, deep convolutional neural network (ConvNet) is the state of the art method. Although previous studies have reported successful attempts on developing and implementing ConvNet on remote sensing application, several issues are not well explored, such as the use of depthwise convolution, final pooling layer size, and comparison between grayscale and RGB settings. The objective of this study is to perform analysis to address these issues. Two feature learning algorithms were proposed, namely ConvNet as the current state of the art for satellite image classification and Gray Level Co-occurence Matrix (GLCM) which represents a classic unsupervised feature extraction method. The experiment demonstrated consistent result with previous studies that ConvNet is superior in most cases compared to GLCM, especially with 3x3xn final pooling. The performance of the learning algorithms are much higher on features from RGB channels, except for ConvNet with relatively small number of features