Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

Download Full-text

Impact of Climate Zone on Impervious Surface Estimation and Mapping

Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas ◽

10.1201/b18836-10 ◽

2015 ◽

pp. 82-105

Keyword(s):

Impervious Surface ◽

Climate Zone ◽

Surface Estimation

Download Full-text

Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13214472 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4472

Author(s):

Tianyu Zhang ◽

Cuiping Shi ◽

Diling Liao ◽

Liguo Wang

Keyword(s):

Image Classification ◽

Data Augmentation ◽

Hyperspectral Image ◽

Classification Performance ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Hyperspectral Image Classification ◽

Feature Maps ◽

Spatial Feature ◽

Global Context

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.

Download Full-text

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Journal of the Optical Society of Korea ◽

10.3807/josk.2016.20.6.752 ◽

2016 ◽

Vol 20 (6) ◽

pp. 752-761

Author(s):

Dabiao Zhou ◽

Dejiang Wang ◽

Lijun Huo ◽

Ping Jia

Keyword(s):

Sparse Representation ◽

Hyperspectral Images

Download Full-text

DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-89-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 89-96 ◽

Cited By ~ 2

Author(s):

Ribana Roscher ◽

Jan Behmann ◽

Anne-Katrin Mahlein ◽

Jan Dupuis ◽

Heiner Kuhlmann ◽

...

Keyword(s):

Support Vector Machines ◽

Sparse Representation ◽

Test Data ◽

Training Data ◽

Hyperspectral Images ◽

Spectral Information ◽

Support Vector ◽

Leaf Spot Disease ◽

Disease Symptoms ◽

Vector Machines

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of <i>Cercospora</i> leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.

Download Full-text