scholarly journals Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

2019 ◽  
Vol 11 (12) ◽  
pp. 1409 ◽  
Author(s):  
Aaron E. Maxwell ◽  
Michael P. Strager ◽  
Timothy A. Warner ◽  
Christopher A. Ramezan ◽  
Alice N. Morgan ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Classification Accuracy ◽  
High Spatial Resolution ◽  
Kappa Statistic ◽  
Data Availability ◽  
Computational Time ◽  
Land Cover Mapping ◽  
Large Area ◽  
Ancillary Data

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents.

Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery

2011 ◽  
Vol 25 (6) ◽  
pp. 1025-1043 ◽  
Author(s):  
Eva Savina Malinverni ◽  
Anna Nora Tassetti ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Emanuele Frontoni ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Object Based ◽  
High Spatial Resolution Imagery

scholarly journals Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels

2020 ◽  
Vol 12 (3) ◽  
pp. 503
Author(s):  
Li ◽  
Chen ◽  
Foody ◽  
Wang ◽  
Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Temporal Frequency ◽  
Land Cover Mapping ◽  
Remote Sensing Images ◽  
Ancillary Data ◽  
Data Set ◽  
Spatio Temporal ◽  
Land Cover Maps

The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.

High-spatial resolution land cover mapping using remotely sensed image

2008 ◽  
Author(s):  
H. S. Lim ◽  
S. AlSultan ◽  
M. Z. MatJafri ◽  
K. Abdullah ◽  
A. N. Alias ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Land Cover Mapping ◽  
Remotely Sensed Image

scholarly journals Geometry Aware Evaluation of Handcrafted Superpixel-Based Features and Convolutional Neural Networks for Land Cover Mapping Using Satellite Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 513
Author(s):  
Dawa Derksen ◽  
Jordi Inglada ◽  
Julien Michel
Keyword(s):  
Feature Extraction ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Land Cover Mapping ◽  
Geometric Accuracy ◽  
Training Set ◽  
Contextual Features ◽  
Thematic Accuracy ◽  
Selection Of

In land cover mapping at a high spatial resolution, pixel values alone are not always sufficient to recognize the more complex classes. Contextual features (computed with a sliding kernel or other kind of spatial support) can be discriminating for certain land cover classes, for example, different levels of urban density, or classes containing heterogeneous pixels, such as orchards and vineyards. However, the reference data used for training the supervised classifier are almost always sparsely labeled, in other words, not every pixel of the training area is labeled. This makes the selection of an appropriate contextual classification method for land cover mapping problematic. Indeed, the current state-of-the art contextual classification model, the Deep Convolutional Neural Network (D-CNN), encounters issues when the geometry of the desired output is absent from the training set. Data-driven methods like D-CNN rely heavily on the availability of extensive training labels to learn both the feature extraction and classification steps. With a sparse training set, sharp corners are rounded, and thin elongated elements may be either thickened, or entirely lost. Alternatively, there are several methods based on the manual selection of contextual features in a chosen neighborhood, guided by the knowledge of the data and past experience from similar problems. Such approaches should not be as sensitive to sparsely labeled data, as they do not rely on any training data for feature extraction. This paper presents a new process for including contextual information in an image classification scheme: the Histogram Of Auto Context Classes in Superpixels (HACCS), which involves classifying an image using the local class histograms as contextual features. These histograms are calculated within superpixels of different sizes in order to provide a multi-scale characterization of the neighborhood, while preserving the geometry of the image objects. This method is evaluated on two data sets presenting different spatial, temporal, and spectral resolutions, and each case is compared with a D-CNN in terms of class accuracy, but also of the quality of the geometry in the produced map. Experiments on the Sentinel-2 time series show that HACCS provides equivalent thematic accuracy compared to the D-CNN, while exhibiting a higher degree of geometric accuracy. On very high spatial resolution imagery (SPOT-7), the D-CNN provides significantly stronger thematic accuracy, but this comes at the cost of a lower level of geometric accuracy.

Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas

2002 ◽  
Vol 26 (2) ◽  
pp. 173-205 ◽  
Author(s):  
S. E. Franklin ◽  
M. A. Wulder
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Data Acquisition ◽  
Spatial Resolution ◽  
Accuracy Assessment ◽  
Land Cover Classification ◽  
Forest Monitoring ◽  
Large Area ◽  
Ancillary Data ◽  
Remote Sensing Methods

Numerous large-area, multiple image-based, multiple sensor land cover mapping programs exist or have been proposed, often within the context of national forest monitoring, mapping and modelling initiatives, worldwide. Common methodological steps have been identified that include data acquisition and preprocessing, map legend development, classification approach, stratification, incorporation of ancillary data and accuracy assessment. In general, procedures used in any large-area land cover classification must be robust and repeatable; because of data acquisition parameters, it is likely that compilation of the maps based on the classification will occur with original image acquisitions of different seasonality and perhaps acquired in different years and by different sensors. This situation poses some new challenges beyond those encountered in large-area single image classifications. The objective of this paper is to review and assess general medium spatial resolution satellite remote sensing land cover classification approaches with the goal of identifying the outstanding issues that must be overcome in order to implement a large-area, land cover classification protocol.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

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