Progressive Band Processing Research Articles

Progressive Compressively Sensed Band Processing for Hyperspectral Classification

Compressive sensing (CS) has recently been demonstrated as an enabling technology for hyperspectral sensing on remote and autonomous platforms. The power, on-board storage, and computation requirements associated with the high dimensionality of hyperspectral images (HSI) are still limiting factors for many applications. A recent work has exploited the benefits of CS to perform HSI classification directly in the compressively sensed band domain (CSBD). Since the required number of compressively sensed bands (CSBs) needed to achieve full band performance varies with the complexity of an image scene, this article presents a progressive band processing (PBP) approach, called progressive CSB classification (PCSBC), to adaptively determine an appropriate number of CSBs required to achieve full band performance, while also providing immediate feedback from progressions of class classification predictions carried out by PCSBC. By taking advantage of PBP, new progression metrics and stopping criteria are also designed for PCSBC. Four real-world HSIs are used to demonstrate the utility of PCSBC.

Progressive Band Processing of Fast Iterative Pixel Purity Index for Finding Endmembers

This letter develops a progressive band processing (PBP) of fast iterative pixel purity index (FIPPI) according to a band sequential acquisition format in such a way that FIPPI can be processed band by band, while band acquisition is ongoing. As a result, PBP-FIPPI can generate progressive profiles of interband changes among PPI counts which allow users to observe significant bands that capture PPI counts. The idea to implement PBP-FIPPI is to use an inner loop specified by skewers and an outer loop specified by bands to process FIPPI. Interestingly, these two loops can also be interchanged with an inner loop specified by bands and an outer loop iterated by growing skewers. The resulting FIPPI is called progressive skewer processing of FIPPI. It turns out that both versions provide different insights into the design of FIPPI.

Recursive Band Processing of Automatic Target Generation Process for Finding Unsupervised Targets in Hyperspectral Imagery

Automatic target generation process (ATGP) has been widely used for unsupervised target detection. However, as designed, it detects targets using full-band information. Unfortunately, on many occasions, various targets can be detected using varying bands, and ATGP can only provide one-shot target detection with all bands being used. This paper develops a new approach which can implement ATGP bandwise in a progressive manner, called progressive band processing of ATGP (PBP-ATGP) so that ATGP can be carried out band by band. Since PBP-ATGP must repeatedly implement orthogonal projections, recursive equations are further derived for PBP-ATGP, to be called recursive band processing of ATGP (RBP-ATGP) which can implement PBP-ATGP recursively. As a result, many advantages can be benefited from RBP-ATGP. Most importantly, RBP-ATGP can generate 3-D interband progressive profiles from band to band that can be used for progressive target detection, a task for which no target detection techniques using full-band information can provide.

Progressive Band Processing of Anomaly Detection in Hyperspectral Imagery

Anomalies are generally unknown and unexpected and cannot be detected with prior knowledge. Consequently, it is highly desirable to have them detected in an unsupervised manner on a timely basis. One way to do so is to perform anomaly detection while the process of data collection is still ongoing, so that weak anomalies will not be dominated and overwhelmed by subsequent detected strong anomalies. This paper presents an approach to progressive band processing of anomaly detection (PBP-AD) band by band according to band sequential (BSQ) format. In other words, anomaly detection can be carried out band by band progressively without waiting for entire bands completely acquired. This significant advantage allows anomaly detection to be implemented in real time in the sense of progressive band processing with the data processing taking place and data being collected at the same time. This capability also paves a way for anomaly detection in future satellite data communication and transmission where the data can be processed and down linked from satellites band by band simultaneously.

Progressive Band Processing of Linear Spectral Unmixing for Hyperspectral Imagery

This paper develops a new approach, called progressive band processing (PBP) of linear spectral unmixing (LSU) which can process data unmixing according to band sequential (BSQ) format. This new concept is quite different from band selection (BS) which must select bands from a fully collected band set based on a band optimization criterion. There are several advantages of using PBP-LSU over BS. In particular, it allows users to perform LSU using available bands without waiting for a complete collection of full bands. In doing so, an innovations information update equation is further derived and can process LSU as its band process is ongoing. To be more specific, LSU can be carried out by PBP by updating unmixed data recursively band by band in the same way that a Kalman filter does for updating data information in a recursive fashion. With such a recursion in nature, PBP is able to process bands of interest which may vary with different applications.

Progressive Band Processing of Constrained Energy Minimization for Subpixel Detection

Constrained energy minimization (CEM) has been widely used for subpixel detection. It takes advantage of inverting the global sample correlation matrix R to suppress background so as to enhance detection of targets of interest. This paper presents a progressive band processing of CEM (PBP-CEM) which can perform CEM for target detection progressively band by band according to band sequential format. In doing so, a new concept, called causal band correlation matrix (CBCM), is introduced to replace the global sample correlation matrix R. It is a global correlation matrix formed by only those bands that were already visited up to the band currently being processed while excluding bands yet to be visited in the future. The proposed PBP-CEM allows CEM to be processed whenever bands are available, without waiting for completing band collection. With such an advantage, CEM has potential in data transmission and communication, specifically in satellite data processing.