Take a Ramble into Solution Spaces for Classification Problems in Neural Networks

International audienc

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis

Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network

The Atlantic rainforest of Brazil is one of the global terrestrial hotspots of biodiversity. Despite having undergone large scale deforestation, forest cover has shown signs of increases in the last decades. Here, to understand the degradation and regeneration history of Atlantic rainforest remnants near São Paulo, we combine a unique dataset of very high resolution images from Worldview-2 and Worldview-3 (0.5 and 0.3m spatial resolution, respectively), georeferenced aerial photographs from 1962 and use a deep learning method called U-net to map (i) the forest cover and changes and (ii) two pioneer tree species, Cecropia hololeuca and Tibouchina pulchra. For Tibouchina pulchra, all the individuals were mapped in February, when the trees undergo mass-flowering with purple and pink blossoms. Additionally, elevation data at 30m spatial resolution from NASA Shuttle Radar Topography Mission (SRTM) and annual mean climate variables (Terraclimate datasets at ∼ 4km of spatial resolution) were used to analyse the forest and species distributions. We found that natural forests are currently more frequently found on south-facing slopes, likely because of geomorphology and past land use, and that Tibouchina is restricted to the wetter part of the region (southern part), which annually receives at least 1600 mm of precipitation. Tibouchina pulchra was found to clearly indicate forest regeneration as almost all individuals were found within or adjacent to forests regrown after 1962. By contrast, Cecropia hololeuca was found to indicate older disturbed forests, with all individuals almost exclusively found in forest fragments already present in 1962. At the regional scale, using the dominance maps of both species, we show that at least 4.3% of the current region’s natural forests have regrown after 1962 (Tibouchina dominated, ∼ 4757 ha) and that ∼ 9% of the old natural forests have experienced significant disturbance (Cecropia dominated)

Airborne Object Detection Using Hyperspectral Imaging: Deep Learning Review

© 2019, Springer Nature Switzerland AG. Hyperspectral images have been increasingly important in object detection applications especially in remote sensing scenarios. Machine learning algorithms have become emerging tools for hyperspectral image analysis. The high dimensionality of hyperspectral images and the availability of simulated spectral sample libraries make deep learning an appealing approach. This report reviews recent data processing and object detection methods in the area including hand-crafted and automated feature extraction based on deep learning neural networks. The accuracy performances were compared according to existing reports as well as our own experiments (i.e., re-implementing and testing on new datasets). CNN models provided reliable performance of over 97% detection accuracy across a large set of HSI collections. A wide range of data were used: a rural area (Indian Pines data), an urban area (Pavia University), a wetland region (Botswana), an industrial field (Kennedy Space Center), to a farm site (Salinas). Note that, the Botswana set was not reviewed in recent works, thus high accuracy selected methods were newly compared in this work. A plain CNN model was also found to be able to perform comparably to its more complex variants in target detection applications

Accelerating Outlier Detection with Uncertain Data Using Graphics Processors

The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2012), Kuala Lumpur, Malaysia, May 29 - June 1, 2012Outlier detection (also known as anomaly detection) is a common data mining task in which data points that lie outside expected patterns in a given dataset are identified. This is useful in areas such as fault detection, intrusion detection and in pre-processing before further analysis. There are many approaches already in use for outlier detection, typically adapting other existing data mining techniques such as cluster analysis, neural networks and classification methods such as Support Vector Machines. However, in many cases data from sources such as sensor networks can be better represented with an uncertain model. Detecting outliers with uncertain data involves far more computation as each data object is usually represented by a number of probability density functions (pdfs).In this paper, we demonstrate an implementation of outlier detection with uncertain objects based on an existing density sampling method that we have parallelized using the cross-platform OpenCL framework. While the density sampling method is a well understood and relatively straightforward outlier detection technique, its application to uncertain data results in a much higher computational workload. Our optimized implementation uses an inexpensive GPU (Graphics Processing Unit) to greatly reduce the running time. This improvement in performance may be leveraged when attempting to detect outliers with uncertain data in time sensitive situations such as when responding to sensor failure or network intrusion.Department of ComputingRefereed conference pape

ISPRS Technical Commission VIII Symposium, Operational RS Applications: Opportunities, Progress and Challenges

Traditionally, forest tree crowns are extracted using airborne or spaceborne hyper-/multi-spectral remotely sensed images or pansharpened images. However, these medium/low spatial resolution images suffer from the mixed pixel problem, and the cost to collect very high resolution image collection is high. Moreover, existing feature extraction techniques cannot extract local patterns from medium/low resolution images. Therefore, super-resolution mapping (SRM) techniques, which generate land-cover maps with finer spatial resolution than the original remotely sensed image, can be beneficial for the extraction of forest trees. The SRM methods can improve the quality of information extraction by combining spectral information and spatial context into image classification problems. In this paper we have improved an adaptive Markov random field approach for super-resolution mapping (MRF-SRM) based on spatially adaptive MRF-SPM to overcome the limitation of equal covariance matrices assumption for all classes. We applied the developed method for mangrove tree identification from multispectral image recorded by QuickBird satellite, where we generated a super-resolution map with the panchromatic image spatial resolution of 0.6 m. Moreover, the performance of the proposed technique is evaluated by employing the simulated image with different covariance matrices for each class. Our experimental results have demonstratedthat the new adaptive MRF-SRM method has increased the overall accuracy by 5.1% and the termination conditions of this method were satisfied three times faster when compared to the state-of-the-art methods

Space-to-speed architecture supporting acceleration on VHR image processing

202310 bcvcVersion of RecordOthersNational Natural Science Foundation of ChinaPublishe