Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

Sturari, Mirco; Frontoni, Emanuele; Pierdicca, Roberto; Mancini, Adriano; Malinverni, Eva Savina; Tassetti, Anna Nora; Zingaretti, Primo

doi:10.1080/22797254.2017.1274572

Cited by 25 publications

(16 citation statements)

References 33 publications

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“…Anyhow, the hard thematic map gives only a general overview of the degradation but it doesn't analyze in detail the colour decay of each pigment in different areas. In order to determine the region in the images where one can localize and quantify the degradation, the process of segmentation has been performed using the ortho-rectified images and the DSM (Sturari et al, 2017). The BAATZ segmentation algorithm divides the image into regions with common characteristics based on key features extracted from the dataset (e.g., geometrical or colorimetric).…”

Section: Change Detection Workflowmentioning

confidence: 99%

Documentation and Detection of Colour Changes of Bas Relieves Using Close Range Photogrammetry

Malinverni

Pierdicca

Sturari

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The digitization of complex buildings, findings or bas relieves can strongly facilitate the work of archaeologists, mainly for in depth analysis tasks. Notwithstanding, whether new visualization techniques ease the study phase, a classical naked-eye approach for determining changes or surface alteration could bring towards several drawbacks. The research work described in these pages is aimed at providing experts with a workflow for the evaluation of alterations (e.g. color decay or surface alterations), allowing a more rapid and objective monitoring of monuments. More in deep, a pipeline of work has been tested in order to evaluate the color variation between surfaces acquired at differentépoques. The introduction of reliable tools of change detection in the archaeological domain is needful; in fact, the most widespread practice, among archaeologists and practitioners, is to perform a traditional monitoring of surfaces that is made of three main steps: production of a hand-made map based on a subjective analysis, selection of a sub-set of regions of interest, removal of small portion of surface for in depth analysis conducted in laboratory. To overcome this risky and time consuming process, digital automatic change detection procedure represents a turning point. To do so, automatic classification has been carried out according to two approaches: a pixel-based and an object-based method. Pixel-based classification aims to identify the classes by means of the spectral information provided by each pixel belonging to the original bands. The object-based approach operates on sets of pixels (objects/regions) grouped together by means of an image segmentation technique. The methodology was tested by studying the bas-relieves of a temple located in Peru, named Huaca de la Luna. Despite the data sources were collected with unplanned surveys, the workflow proved to be a valuable solution useful to understand which are the main changes over time.

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Section: Change Detection Workflowmentioning

confidence: 99%

Documentation and Detection of Colour Changes of Bas Relieves Using Close Range Photogrammetry

Malinverni

Pierdicca

Sturari

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It must be noted that the approach of combining data streams is nothing new, see for example [26,27]. It is also important to realize that the DSM extraction strategy, although robust, is costly in terms pre-processing load, which can impede the application of this technique in onboard deployment on real-time analysis platforms such as UAVs (see [28] for a related example).…”

Section: The Dsm/radiometric Approach To Canopy Segmentationmentioning

confidence: 99%

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

et al. 2019

Self Cite

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One of the most challenging problems in precision agriculture is to correctly identify and separate crops from the soil. Current precision farming algorithms based on artificially intelligent networks use multi-spectral or hyper-spectral data to derive radiometric indices that guide the operational management of agricultural complexes. Deep learning applications using these big data require sensitive filtering of raw data to effectively drive their hidden layer neural network architectures. Threshold techniques based on the normalized difference vegetation index (NDVI) or other similar metrics are generally used to simplify the development and training of deep learning neural networks. They have the advantage of being natural transformations of hyper-spectral or multi-spectral images that filter the data stream into a neural network, while reducing training requirements and increasing system classification performance. In this paper, to calculate a detailed crop/soil segmentation based on high resolution Digital Surface Model (DSM) data, we propose the redefinition of the radiometric index using a directional mathematical filter. To further refine the analysis, we feed this new radiometric index image of about 3500 × 4500 pixels into a relatively small Convolution Neural Network (CNN) designed for general image pattern recognition at 28 × 28 pixels to evaluate and resolve the vegetation correctly. We show that the result of applying a DSM filter to the NDVI radiometric index before feeding it into a Convolutional Neural Network can potentially improve crop separation hit rate by 65%.

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“…Compared with feature-level fusion, multisource datasets are separately classified and then fused or integrated in the process of decision-level fusion to generate the final classification results. Sturari et al [16] proposed a decision-level fusion method for the fusion of LiDAR and multispectral optical data, where the LiDAR classified objects were used as a posteriori in the object rule-based winner-takes-all fusion step.…”

Section: Introductionmentioning

confidence: 99%

Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network

Feng

Zhu

Yang

et al. 2019

IJGI

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Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on "Squeeze-and-Excitation Networks"). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy.

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Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

Cited by 25 publications

References 33 publications

Documentation and Detection of Colour Changes of Bas Relieves Using Close Range Photogrammetry

Documentation and Detection of Colour Changes of Bas Relieves Using Close Range Photogrammetry

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network

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